Fault Detection and Condition Assessment using
Vibration Analysis and Failure Mode and Effect Analysis
Daníel Jónsson
Faculty of Industrial Engineering, Mechanical Engineering and Computer Science
University of Iceland 2020
Fault Detection and Condition Assessment using
Vibration Analysis and Failure Mode and Effect Analysis
Daníel Jónsson
60 ECTS thesis submitted in partial fulfillment of a
Magister Scientiarum degree in Mechanical Engineering
MS Committee
Magnús Þór Jónsson
Rafn Magnús Jónsson
Thomas Philip Rúnarsson
Faculty of Industrial Engineering, Mechanical Engineering and Computer science.
School of Engineering and Natural Sciences
University of Iceland
Reykjavik, May 2020
Fault Detection and Condition Assessment using Vibration Analysis and Failure Mode and
Effect Analysis.
Fault Detection & Condition Assessment.
60 ECTS thesis submitted in partial fulfillment of a Magister Scientiarum degree in
Mechanical Engineering
Copyright © 2020 Daníel Jónsson
All rights reserved
Faculty of Industrial Engineering,
Mechanical Engineering and Computer Science
School of Engineering and Natural Sciences
University of Iceland
VR II, Hjarðarhagi 6
107, Reykjavik
Iceland
Telephone: 525 4000
Bibliographic information:
Daníel Jónsson, 2020, Fault Detection and Condition Assessments using Vibration
Analysis and Failure Mode and Effect Analysis, Master’s thesis, Faculty of Industrial
Engineering, Mechanical Engineering and Computer Science, University of Iceland.
Printing: Háskólaprent, Fálkagata 2, 107 Reykjavik
Reykjavik, Iceland, May 2020
Abstract
Modern day industry often depends on rotating machinery operating constantly all year
around. A well-organized and planned maintenance strategy is one of the key features to
obtain a constant and safe operation. To conduct a successful maintenance strategy its
crucial to be able to monitor machines condition when in operation. Numerous ways are
used to monitor machines conditions where one of the main methods is vibration
monitoring and analysis. The reason that initiated this thesis was problematic operation in
one of the main centrifugal fan’s bearings, in one the fume treatment plants at Norðurál
aluminum factory. The main objective of this study is to design and develop a vibration
measurement and analysis system. The system is used to collect vibration data, from the
centrifugal fan’s plummer block bearings, and utilizes known vibration analysis methods to
assess the bearings current condition and to estimate its remaining useful life. Failure
Mode and Effect Analysis (FMEA) is performed on the centrifugal fan components and
setup to establish some indication to pinpoint the bearing problem causes.
The results from the vibration analysis conducted in this study, using the measurements
gathered from the centrifugal fan’s plummer block bearings, showed various fault
indications. The remaining useful life estimation indicated to short bearing life, although
there are some parts of the methodology used that need adjustments and/or modifications
to fit better for this thesis.
Útdráttur
Í nútíma iðnaði er mikið treyst á vélar og tæki sem þurfa að ganga stanslaust allt árið um
kring. Eitt af lykilatriðum til að viðhalda stöðugum og öruggum rekstri er vel skipulögð
viðhaldsáætlun. Til að geta starfrækt vel heppnaða viðhaldsáætlun er nauðsynlegt að geta
fylgst með ástandi vélbúnaðar meðan hann er í rekstri. Margskonar leiðir eru notaðar til að
fylgjast með ástandi vélbúnaðar, þar sem ein af aðal aðferðunum er að fylgjast með titring
og framkvæma titringsgreiningar. Kveikjan að þessu verkefni var vandræða ástand á einni
af búkkalegum í einum aðalblásara reykhreinsivirkis eitt í álveri Norðuráls. Eitt aðal
markmið þessa verkefnis er að hanna og þróa titrings mælingar og greiningar kerfi. Kerfið
er notað til að safna titringsmælingum, frá búkkalegum aðalblásarana, og nýta þekktar
aðferðir til þess að greina mælingarnar til að leggja mat á núverandi ástand og áætla líftíma
legana. Framkvæmd er FMEA greining á íhlutum og skipulagi blásarans til þess að draga
fram vísbendingar um ástæður bilana.
Niðurstöður titringsgreiningar sem framkvæmd var í þessu verkefni, með mælingum
safnað frá búkkalegum aðalblásaranna, sýndi vísbendingar mismunandi bilanir.
Líftímagreining gaf til kynna of stuttan líftíma lega, þó svo að ákveðna hluti
aðferðarfræðinnar þurfi að stilla og/eða breyta til að passa betur að verkefninu.
v
Dedication
I would like to dedicate this thesis foremost to my family, my wife Heiða and my sons
Sigmar and Daníel. With their emotional and moral support, I got the strength to keep
pushing forward and finish this thesis.
vi
Table of Contents
List of Figures ............................................................................................................... viii
List of Tables ................................................................................................................ xiv
Nomenclature................................................................................................................. xv
Acknowledgements ...................................................................................................... xvii
1 Introduction .............................................................................................................. 19
2 Background ............................................................................................................... 21
2.1 Maintenance Strategies ...................................................................................... 21 2.1.1 Breakdown Maintenance ........................................................................... 21
2.1.2 Preventive Maintenance ............................................................................ 21 2.1.3 Predictive Maintenance ............................................................................. 22
2.1.4 Proactive Maintenance .............................................................................. 22 2.2 Failure and Risk Assessment .............................................................................. 23
2.2.1 Failure Mode and Effect Analysis ............................................................. 23 2.2.2 Root Cause Analysis ................................................................................. 28
2.2.3 Fault Tree Analysis ................................................................................... 30 2.3 Condition Monitoring ........................................................................................ 34
2.3.1 Vibration Measurements and Transducers ................................................. 36 2.3.2 Oil/Lubricant Analysis .............................................................................. 43
2.3.3 Thermography .......................................................................................... 44 2.3.4 Performance Analysis ............................................................................... 45
3 Vibration Signals....................................................................................................... 47 3.1 Theory and Classification................................................................................... 47
3.1.1 Vibration Theory ...................................................................................... 47 3.1.2 Vibration Signal Classification.................................................................. 51
3.2 Vibration Signals from Rotating Machinery ....................................................... 53 3.2.1 Shaft Frequency and its Harmonics ........................................................... 54
3.2.2 Rolling Element Bearings ......................................................................... 57 3.2.3 Bladed Machines ...................................................................................... 61
4 Signal Processing and Analysis................................................................................. 63 4.1 Signal Processing Techniques ............................................................................ 63
4.1.1 Signal Conditioning .................................................................................. 63 4.1.2 Fourier Analysis ....................................................................................... 70
4.1.3 Envelope Analysis .................................................................................... 75 4.2 Detection, Diagnostics, and Prognostics ............................................................. 76
4.2.1 Fault Detection ......................................................................................... 77 4.2.2 Diagnostic Techniques .............................................................................. 78
4.2.3 Prognosis .................................................................................................. 81
vii
5 Case Study at Norðurál Aluminum Plant ................................................................ 87
5.1 Fan Setup and History ........................................................................................ 88 5.1.1 Components and Operational Conditions ................................................... 88
5.1.2 Plummer Block Bearings Maintenance History ......................................... 90 5.2 Vibration Measurement and Analysis System ..................................................... 92
5.2.1 Localized Equipment................................................................................. 93 5.2.2 Portable Equipment ................................................................................. 100
5.2.3 Vibration Analysis Software.................................................................... 102 5.3 Assessments and Condition Measurements ....................................................... 104
5.3.1 Assessing Causes for Short Bearing Life ................................................. 104 5.3.2 Assessing Condition and Fault Development using Vibration Analysis ... 109
5.3.3 Remaining Useful Life Estimation .......................................................... 124
6 Conclusions .............................................................................................................. 127
Bibliography ................................................................................................................. 129
Appendices ................................................................................................................... 133
viii
List of Figures
Figure 1: Example of generic FMEA worksheet. ............................................................. 25
Figure 2: Example of severity and occurrence scales. ...................................................... 25
Figure 3: Example of Process FMEA detection scale [5]. ................................................. 26
Figure 4: The logical relationship between FMEA elements [4]. ...................................... 27
Figure 5: Example of a RCA procedure. .......................................................................... 28
Figure 6: Example of a "Five-Whys" problem and solution.............................................. 29
Figure 7: Example of the Cause and Effect Diagram. ....................................................... 30
Figure 8: Example of fault tree event and gate symbols. .................................................. 32
Figure 9: Example of hydrolic system and corresponding fault tree. ................................ 34
Figure 10: Scheme of a vibration measurement and analysis system. ............................... 36
Figure 11: Comparison between Absolute- and Relative Measurements. .......................... 37
Figure 12: Comparison between mounting setups for proximity probes. .......................... 38
Figure 13: Scheme of a Eddy current based proximity probe system. ............................... 39
Figure 14: Two basic types of velocity transducers, Magnet-In-Coil and Coil-In-
Magnet. ......................................................................................................... 40
Figure 15: Three main types of accelerometers, Compression, Bending, and Shear. ......... 40
Figure 16:Typical compression type piezoelectric accelerometer with top connection. ..... 41
Figure 17: Methods for mounting accelerometers. ........................................................... 42
Figure 18: Thermal images of faulty components. ........................................................... 44
Figure 19: Example of a Mass-Spring-Damper System. ................................................... 47
Figure 20: Harmonic cycle, locus of mass-spring motion with respect to time.................. 48
Figure 21: Relations between displacement, velocity, and acceleration. ........................... 49
Figure 22: Vibration spectrum displayed in time domain and frequency domain. ............. 50
Figure 23: Amplitudes comparison for a single harmonic wave. ...................................... 50
ix
Figure 24: Examples of different signal types and their spectral content. .......................... 52
Figure 25: Simplified schematic picture of centrifugal fan setup. ..................................... 53
Figure 26: Comparison between static and dynamic unbalance. ....................................... 54
Figure 27: Comparison between possible misalignments in coupled connections. ............ 55
Figure 28: Typical frequency spectrums for parallel and/or angular misalignments. ......... 56
Figure 29: Examples of mechanical looseness and typical vibration response. .................. 56
Figure 30: Nomenclature of a ball bearing and common types of roller and ball
bearings. ........................................................................................................ 57
Figure 31: Bearing life model, showing common frequency spectrums for each stage. ..... 59
Figure 32: Frequency spectrums of common cases on bladed machines. .......................... 61
Figure 33: Basic non-inverting operational amplifier circuit. ............................................ 64
Figure 34: Schematic diagram of a Delta-Sigma type analog-to-digital converter [14]. ..... 64
Figure 35: Frequency aliasing due to an inadequate sampling rate. ................................... 67
Figure 36: Coparison between response curves for the four main filter types. ................... 68
Figure 37: Leakage error appearing in frequency spectrum, for the non-periodic
signal. ............................................................................................................ 69
Figure 38: Comparison between Rectangular, Hanning, and Flat Top weighting
functions. ....................................................................................................... 70
Figure 39: Matrix representation of the DFT. ................................................................... 73
Figure 40: Matrix B, a modified version of matrix Wkn, with rows shifted. ....................... 74
Figure 41: Factorization of matrix B into three factor matrices X, Y, and Z. .................... 75
Figure 42: Envelope analysis procedure using the Hilbert transform method. ................... 76
Figure 43: Comparison of two spectra, with direct digital comparison, with no
change in operational condition...................................................................... 78
Figure 44: The process of calculating the Spectral Kurtosis of a simulated bearing
vibration signal with localized fault on inner raceway. ................................... 79
Figure 45: Fast Kurtogram for measurement number 2150 of bearing number 3 from
the IMS-Dataset 1. ......................................................................................... 80
Figure 46: Comparison on calculated SK with optimal bandpass filter and with no
filter. .............................................................................................................. 81
x
Figure 47: Trend of kurtosis for bearing 3 from IMS dataset no.1, inner raceway fault
develops at end of lifetime. ............................................................................ 83
Figure 48: Prognostic method as proposed by J.W.Hines & A. Usynin in [29] ................. 85
Figure 49: Norðurál aluminum plant and the location of the pot rooms and FTP’s. .......... 87
Figure 50: Sideview of one of the centrefugal fans and a list of it’s major components .... 88
Figure 51: Current bearing setup and impeller for fans in FTP-1 and FTP-2..................... 89
Figure 52: Rough estimation on force distribution along the driveshaft and the
bearings. ........................................................................................................ 90
Figure 53: Sooted grease in main fan 2 plummer block bearings, in FTP-1. ..................... 91
Figure 54: Damages found in main fan 2 Fan DE bearing, during grease change.............. 92
Figure 55: Electrical circuit for a fifth-order Butterworth filter without any
amplification, where R and C denotes the resistors and the capacitors............ 93
Figure 56: The electrical circuit for fifth-order Butterworth low-pass filter with 1000
Hz cut-off frequency and 22 dB amplification. .............................................. 95
Figure 57: Power circuit for a single accelerometer and a low-pass filter. ........................ 96
Figure 58: Filters theoretical respone displayed on Bode diagram. ................................... 96
Figure 59: The real frequency response of the filters, measured with the oscilloscope. ..... 97
Figure 60: The compact filter unit, with six 1000 Hz low-pass Butterworth filters. .......... 97
Figure 61: NI USB-6000 ADC dimensions and key specifications. .................................. 98
Figure 62: CMCP1100 accelerometer dimensions and key specification. ......................... 98
Figure 63: Localized equipment fitted inside an electric cabinet....................................... 99
Figure 64: Locacalized equipment at site, with electric cabinet connected to 230 V
power grid and accelerometers mounted on bearing housings with
magnetic bases. ........................................................................................... 100
Figure 65: DT9837B dynamic signal analyzer and its key specifications........................ 100
Figure 66: High frequency accelerometer and mounting parts dimensions and setup. ..... 101
Figure 67: Accelerometers mounted on bearing housings, fitted in the eyebolts
threaded holes. ............................................................................................ 101
Figure 68: Graphical user interface for the vibration measurement and analysis
software. ..................................................................................................... 102
xi
Figure 69: Flowchart of measurement and saving process. ............................................. 102
Figure 70: Flowchart for the single measurement analysis process, (continued
flowchart from Figure 69). ........................................................................... 103
Figure 71: Flowchart for the multiple measurement analysis process, (continued
flowchart from Figure 70). ........................................................................... 104
Figure 72: Development of the pot rooms amperage since the year 2010, and time of
bearing change on main fans 1 and 2 in FTP-1. ............................................ 108
Figure 73: Fan DE bearing frequency spectrum, on main fan 1 in FTP-1, from July
2019. ........................................................................................................... 112
Figure 74:Fan DE bearing frequency spectrum, on main fan 1 in FTP-1, from May
2020 ............................................................................................................ 112
Figure 75: Vibration signals envelope comparison between first and last
measurements on Fan DE bearing on main fan 1, in FTP-1. ......................... 113
Figure 76: FTP-1 main fan 1 Fan DE bearing, waterfall graph of the 1.5 kHz to 25
kHz frequency spectrums, from July 2019 to May 2020. .............................. 114
Figure 77: Spectrogram of the first and last vibration measurements, on Fan DE
bearing. ........................................................................................................ 114
Figure 78:Fan DE bearing frequency spectrum, from July 2019 (Bearing New). ............ 115
Figure 79: Envelope spectrum of Fan DE bearing, taken in July 2019 (Bearing New). ... 116
Figure 80: Fan DE bearing frequency spectrum for main fan 2 in FTP-1, from May
2020. ........................................................................................................... 116
Figure 81: Envelope spectrum of Fan DE bearing, taken in May 2020. .......................... 117
Figure 82:Vibration signals envelope comparison between first and last
measurements performed on Fan DE bearing on main fan 2, in FTP-1. ........ 117
Figure 83: Fan DE bearing, waterfall graph of the 1 kHz to 30 kHz frequency
spectrums, from July 2019 to May 2020, for main fan 2 on FTP-1. .............. 118
Figure 84: Frequency spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-1. ............................... 119
Figure 85: Envelope spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-1. ............................... 120
Figure 86: Vibration signals envelope comparison between first and last
measurements performed on Fan DE bearing on main fan 4, in FTP-1. ........ 120
Figure 87: Frequency spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 3, in FTP-2 ................................ 121
xii
Figure 88: Frequency spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-2. ............................... 122
Figure 89: Envelope spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-2. ............................... 123
Figure 90: Vibration signals envelope comparison between first and last
measurements performed on Fan DE bearing on main fan 4, in FTP-2. ........ 123
Figure 91: Overall RMS vibration level for all measurements performed on the Fan
DE bearing, on main fan 2 in FTP-1. ........................................................... 124
Figure 92: Overall RMS vibration level for all measurements after bearing change,
with linear and exponetial trending curves. .................................................. 125
Figure 93: Mean frequency, Skewness, Kurtosis, and Crest factor for all
measurements performed on the Fan DE bearing, on main fan 2 in FTP-
1. ................................................................................................................. 126
Figure 94: ISO 10816-3 vibration severity classification. ............................................... 133
Figure 95: General vibration severity chart, for rotating machinery. ............................... 134
Figure 96: DT9837B Dynamic Signal Analyzer's block diagram. .................................. 141
Figure 97: Visualization on GUI operation, Select data type, load data, and select
amplitude representation. ............................................................................. 143
Figure 98: Visualization on GUI operation, Selecting sensor and weighting window. .... 144
Figure 99: Visualization on GUI operation. Bearing fault frequencies loaded from
database and manually calculated, scale settings, and display of bearing
fault frequencies and geometric parameters. ................................................ 145
Figure 100: Visualization on GUI operation. Measurement settings, CI selection,
Spectrogram setting, and Envelope settings. ................................................ 146
Figure 101: Frequency spectrum comparison between July 2019 and May 2020 for
the Fan ND bearing on main fan 1, in FTP-1. .............................................. 150
Figure 102: Vibration signals envelope comparison between July 2019 and May
2020 for the Fan ND bearing on main fan 1, in FTP-1. ................................ 150
Figure 103: Frequency spectrum comparison between July 2019 and May 2020 for
the Fan ND bearing on main fan 2, in FTP-1. .............................................. 151
Figure 104: Vibration signals envelope comparison between July 2019 and May
2020 for the Fan ND bearing on main fan 2, in FTP-1. ................................ 151
Figure 105: Fan DE bearing, waterfall graph of the 1 kHz to 30 kHz frequency
spectrums, from July 2019 to May 2020, for main fan 4 in FTP-1................ 152
xiii
Figure 106: Frequency spectrum comparison between July 2019 and May 2020 for
the Fan ND bearing on main fan 4, in FTP-1 ................................................ 152
xiv
List of Tables
Table 1: Accelerometer advantages and disadvantages, comparison between types. ......... 42
Table 2: Span, Dynamic Range, and Signal-to-Noise comparison between common
bit size Analog-to-Digital Converters. ........................................................... 66
Table 3: Date of bearing changes for the plummer block bearings on main fans in
fume treatment plants 1 and 2, with bearings running hours at each time. ...... 90
Table 4: Calculated and used sizes of capacitors. ............................................................. 95
Table 5: Accelerometers amplitude factors. ..................................................................... 99
Table 6: Failure modes, effects, and causes with highest RPN for main fan 2 in FTP-
1, summ-up from the full FMEA-worksheet listed in Table 17, in
appendix F................................................................................................... 106
Table 7: Initial condition assessments on plummer block bearings on main fans in
FTP-1. ......................................................................................................... 110
Table 8: Initial condition assessments on plummer block bearings on main fans in
FTP-2. ......................................................................................................... 110
Table 9: BPFO and BPFI amplitudes comparison between measurements ..................... 118
Table 10: Information on main fan plummer block bearings data, operational data,
and bearing fault frequencies. ...................................................................... 135
Table 11: Fan DE and ND Bearings Lubricant Technical Data (optained from SKF
website). ...................................................................................................... 136
Table 12: Maintenance history for fume treatment plants 1 and 2, for bearings and
grease change interval, for the years 2008 to the summer of 2019. ............... 137
Table 13: Components list for the localized equipment low-pass filter unit (6 filters
in one box). ................................................................................................. 138
Table 14: NI-USB-6000 Analog-to-Digital converter specifications .............................. 139
Table 15: Vibration transducers specifications for the localized equipment. ................... 140
Table 16: Wilcoxon model 736T high frequency accelerometer specifications. .............. 142
Table 17: FMEA-worksheet for main fan 2 in FTP-1 (spreads over three pages). ........... 147
xv
Nomenclature
ADC Analog to Digital Converter
BPF Blade Pass Frequency
BPFI Ball Pass Frequency Inner
BPFO Ball Pass Frequency Outer
BSF Ball Spin Frequency
CED Cause and Effect Diagram
CF Crest Factor
CoM Center of Mass
CPB
DE
Constant Percentage Bandwidth
Drive End
DFT
FD-CI
Discrete Fourier Transform
Frequency-Domain Condition Indicator
FFT Fast Fourier Transform
FMEA Failure Mode and Effect Analysis
FP Failure Probability
FTA
FTP
Fault Tree Analysis
Fume Treatment Plant
FTF
GUI
Fundamental Train Frequency
Graphical User Interface
MTTF
ND
Mean Time to Failure
Non-Drive end
R Reliability
RCA Root Cause Analysis
xvi
REB Rolling Element Bearing
RMS Root Mean Square
RPN Risk Priority Number
RUL
SE
Remaining Useful Life
Spectral Entropy
SK Spectral Kurtosis
SNR Signal to Noise Ratio
STFT
TD-CI
TF-CI
Short Time Fourier Transform
Time-Domain Condition Indicator
Time-Frequency-domain Condition Indicator
xvii
Acknowledgements
I would first like to thank my thesis instructor professor Magnús Þór Jónsson of the
Mechanical engineering department at University of Iceland. Every time I ran into trouble
or had questions regarding the thesis Prof. Jónsson was always willing to help me and
guide me in the right direction.
I would also like to thank Norðurál ehf. for their financial support and financing on the
measurement equipment used during the case study. Without their support this thesis
would surely never been possible.
I would also like to thank Mr. Vilhjálmur Ívar Sigurjónsson for his help during the
preparation of the measurement equipment and all the obstacles we had to overcome to get
everything working.
Finally, I would like to thank the experts, MSc. Rafn Magnús Jónsson, MSc. Einar Friðgeir
Björnsson, and Mr. Bjarni Tryggvason for their contribution on collecting information
during the case study within this thesis.
19
1 Introduction
In today’s world there are countless variations of industries with different process plants
with all types of machinery. Running a good maintenance strategy is one of the most
important part to ensure a good, safe, and reliable operation. The methodology for
assessing the condition of a machinery has evolved greatly over the last decades regarding
techniques, equipment, digital instrument, and computers. Despite decades of experience
and effort we are still trying to achieve the technique of complete prognostics. Monitoring
the condition of machinery vibration analysis is a major part of trying to achieve that goal.
Originally machines were run till they stopped because some component of the machine
broke down, this type of maintenance is called Breakdown maintenance. Next maintenance
strategy was so-called Preventive maintenance, which is based on performing maintenance
work in predetermined intervals so that there is a little chance on failure between repairs.
Predictive maintenance came thereafter, where the philosophy is based on scheduling
maintenance activities from the current condition of the machine. To utilize this
philosophy, it is crucial to be able to determine the internal condition while the machine is
still in operation. There are two main ways to determine the condition of an operating
machine and they are vibration analysis and oil analysis [1], although other methods are
also known.
In modern industry there is a high demand for companies to obtain a reliable operation and
most of all safe for personnel and its surroundings. With constantly increasing complexity
of machinery and their components multiple methods have been developed in last decades
to support and help companies to achieve this goal. Most of these methods are constructed
to identify and locate possible failure modes and to understand the interconnections
between them to try to eliminate them or reduce their affects. To be able to utilize these
techniques, companies must be willing to modify and adjust their operation accordingly to
improve the operation reliability and safety.
Industrial processes usually contain multiple rotating machines working individually or
together, forming complex systems with multiple functions. To be able to assess the
operational condition of each machine or its components it is essential to extract
information that give some indication on the current condition. One of the corner stones in
condition-based maintenance is measuring machines vibration level and is one of the most
widely used method to monitor machines condition. Vibration analysis utilizes the fact that
rotating machines in operation always generate some sort of vibration and to exploit the
features of vibration analysis it is crucial to be able to identify normal operation from
faulty operation.
The vibration generated by a machine can often be linked to periodic events happening
inside the machine, like the rotating driveshaft, meshing gearteeth, rolling element
bearings, and so on. To be able to identify and isolate the vibration source many frequency
analysis techniques have been developed. These methods were designed to detect and
diagnose faulty states and provide information that is utilized for prognosis. To be able to
extract the condition information from a vibration data, and since vast majority of the
20
vibration analysis techniques used today are performed with the help of computers, signal
processing is needed. Signal processing is used to classify all the steps performed on the
vibration data e.g. analog filtering, Analog-to-Digital conversion, windowing, analyzing,
displaying, etc. With computer power constantly increasing the ability to exploit these
methods has increased greatly.
The main objectives in this study is divided into two parts, were the first part is to utilize a
known failure and risk assessment method to identify causes for unusual short operation on
a machine. The second part is to design and develop a vibration measurement and analysis
system. The system is used to perform and collect vibration measurements and to perform
vibration analysis with methods to evaluate machine current condition and also to estimate
its remaining useful life.
To explore these objectives, a case study at Norðuál aluminum plant was performed, where
a large centrifugal fan in one of the companies fume treatment plants has been having
unusual short bearing life. A failure mode and effect analysis is performed on the fan and
its components to establish the cause for the short bearing life. The vibration measurement
and analysis system was put to use to collect vibration measurements and analyze them, to
perform condition assessment on the bearings and to see if there are any factors in the
vibration data to identify and locate the causes for this problem that the company is having.
The thesis is constructed as follows. Chapter 2 is a background review on known
maintenance strategies, failure and risk assessment methods, and general discussion on
condition monitoring and its techniques and equipment. In Chapter 3 the theory on
vibration signals and their classification is presented along with discussion on vibration
signals generated by rotating machinery. In Chapter 4 the signal processing methods used
in this thesis are presented. In Chapter 5 the case study is presented along with the process
of designing and developing the vibration measurement and analysis system. Then finally
Chapter 6 contains general discussion and conclusions.
21
2 Background
In this chapter the most common maintenance strategies used in today’s industry will be
discussed. There are many failure and risk assessments methods known to be practiced for
all sorts of industries and they all have similar objectives. That is to support companies, no
matter what industry they are in, to obtain a reliable operation. The methods, Failure mode
and effect analysis, Root cause analysis, and Fault tree analysis will be introduced.
Common condition monitoring methods and their advantages will be introduced, where
vibration measurements and transducers used when conducting vibration measurements
will be discussed, along with oil analysis, thermography, and performance analysis.
2.1 Maintenance Strategies
There are many maintenance strategies being used in today’s industry and it depends
greatly on the industry or nature of the operation which strategy is used. Most widely
known maintenance strategies today are Breakdown-, Preventive-, Predictive-, and
Proactive-Maintenance.
2.1.1 Breakdown Maintenance
Breakdown maintenance is the most traditional method, where machines are simply run
until they stop or break down because some part of that machine gets damaged. With this
type of maintenance, there is a high possibility of getting the longest time between failures.
The problem with this is when failure occurs it can be catastrophic which lead to multiple
failures and possibly total breakdown of the machine. Breakdown maintenance is
especially inconvenient where multiple machines work together e.g. in a process line
where the whole line stops if one component breaks down. There is also a possibility that
when one component breaks down it damages other parts of the process line. Resulting in a
great increase in repair- and downtime which lead to higher repair cost. In many industries
the downtime is the most expensive factor because it leads to a production loss which
sometimes is much higher than a single machine in the process line. For complex
industries this type of maintenance is hardly ever used, unless there is a backup system.
For less complex industries there is still room for breakdown maintenance e.g. factories
that uses many small machines to produce the same product, and where production rate is
not affected much if one machine breaks down and the failure is unlikely to be catastrophic
[1].
2.1.2 Preventive Maintenance
This type of maintenance is carried out by repairing or changing individual parts of a
machine at regular intervals, usually based on running hours of a machine or calendar days,
so that it is unlikely that the machine breaks down in the meantime. Intervals are often
decided so that the chance of a breakdown of failure between repairs is close to 1-2% [1].
This results that most of the machines could have run two or three times longer between
22
repairs [2]. This philosophy is good for machines that do not run continuously, and the
main advantage is the ability to plan maintenance well ahead and performed at a
convenient time considering production. The risk of catastrophic failure is also greatly
reduced and therefore personal safety of the production is increased. The disadvantages are
that there is still a possibility of unforeseen failures to occur and usually there is too much
maintenance work performed on machines. Using this method means that sometimes a
perfectly good component of a machine is changed for a new one, resulting in a higher
spare part cost. With unnecessary maintenance work the risk of diminished performance
increases and introduces failures that otherwise would not have happened [3].
2.1.3 Predictive Maintenance
Predictive maintenance, also called ‘Condition-Based Maintenance’, is as the name
indicates a method that uses condition measurements to predict for potential failure in a
machine. The condition of machines components is periodically monitored and when
faulty trends start to appear, a maintenance work is scheduled. The method requires that
the maintenance department has access to reliable condition monitoring technique with the
ability to give a good assessment on the current condition and also reasonable estimation
on the remaining lifetime [1]. An obvious advantage is the ability to plan a work directly
from the condition of the machine, meaning that the maintenance work is performed at the
most convenient time. Usually machines components, like bearings, have some indication
that a failure is imminent sometimes weeks or months. Meaning that all spare parts and
materials needed are ordered in time and the work planned and the machine stopped at the
optimum time. Resulting in reduction in the need for large inventory of spare parts. For
over 40 years this method has been used with some success, but initially available
monitoring techniques were limited and surely not always used correctly. In the last 20 or
so years this method is recognized to be most suitable for most cases [1]. A small
disadvantage is that maintenance work is often performed unnecessary because of a wrong
failure analysis. If a company is going to use this type of maintenance, it is essential that
they use specialized equipment suiting their production and properly train their
maintenance crew. For this strategy to work the management must support their
maintenance department and also provide all the necessary equipment and regular training
courses to maintain knowledge within the company [3].
2.1.4 Proactive Maintenance
Proactive maintenance utilizes all the aspects of predictive maintenance and its techniques
but adds the analyzing the root cause of the failure. The fundamental objective of this type
of maintenance is to analyze and perform proactive measures to prevent these failures to
happen again. The strategy uses root cause analysis (RCA) to detect and pinpoint the
problems that cause failure, also it ensures that right techniques are used for installation
and repair. One advantage is it can pinpoint the need for modification or redesign on a
machine to ensure some failure does not happen again [3].
23
2.2 Failure and Risk Assessment
The demand for a reliable and safe operation is increasing each year at the same time
machineries and their components are getting more complex. This causes problems for
companies to keep up with safe and reliability of their operation. Operational reliability
and safety are conventionally accomplished with testing and modification on the operation,
but also with well trained workers both in production and in maintenance. Murphy’s Law
states “If anything can go wrong, it will” assuming that’s true, companies must try to do
everything in their power to prevent that. There are many methods available to help
companies establish safe and reliable operation, and to list just few we have,
• Failure Modes and Effect Analysis (FMEA)
• Root Cause Analysis (RCA)
• Fault Tree Analysis (FTA)
These methods are used in all sorts of industries at all stages of production from the
development or design to manufacturing.
2.2.1 Failure Mode and Effect Analysis
FMEA was first introduced in 1949 by the U.S. Armed Forces with introduction of
Military Procedures document (MIL-P)-1629, “Procedures for Performing a Failure Mode
Effect and Criticality Analysis”. In the 1960s it was used in the Apollo space program to
minimize risk because of the small sample size. Ford Motor Company introduced FMEA
to the automotive industry in the 1970s for safety and regulatory purposes. Automotive
industry started implementing FMEA in the 1980s with standardizing the structure and
methods. FMEA is now used in vast variety of industries throughout the world [4]
FMEA Main Objective
FMEA primary objective is in general to improve the design of the system, subsystems,
components, manufacturing process, and operation. Many other objectives are for
conducting FMEA like,
• Identify and prevent all safety hazards that can occur
• Try to minimize production loss
• Increase production performance
• Implement changes to production- and/or manufacturing processes
• Identify process characteristics
• Develop plans for preventive maintenance strategies [4]
It is important to understand that FMEA is a powerful tool to achieve these objectives, but
there are limitations too FMEA because it does not model the interactions between failures.
If it’s necessary to understand or model the relationships between failures and their causes
and effect a FTA is most likely the proper tool, it is often used with FMEA [4].
24
FMEA Types
There are three types of FMEA that are most common, they are:
• System FMEA
• Design FMEA
• Process FMEA
System FMEA is the highest level of analysis where entire system with all its subsystems
and components is analyzed. It focuses on all system related deficiencies e.g. safety,
integration, interactions between system and other systems, interaction between
subsystems, interaction between subsystems components, interaction with surrounding,
and human interference. Basically, all issues that can affect the overall system not to
function properly. Unique functions and relationships for the system is the focus in system
FMEA [4].
Design FMEA focuses on the design aspect of a product, usually at the subsystems or their
component. Where design-related deficiencies and improvement on the design is the focus
especially considering safety and reliability during operation [4].
Process FMEA focuses on the manufacturing process and how to improve it to ensure that
a product is manufactured according to design requirements. The aim is also to achieve
that goal in a safe way with as little downtime as possible and best possible efficiency.
Process FMEA includes operations in manufacturing and assembly, shipping of products
and incoming materials and parts, storage, labeling, and tool maintenance [4].
FMEA is a systemized group of actions designed to recognize potential failure modes
within a system and evaluate them by severity and probability. Understand the effects
failures can cause on the system and assess the potential risk they could cause, then
prioritize so right actions are carried out [4]. Too utilize FMEA to its fullest it is ideal that
it is carried out by a team of experts with different specialty. Whether it is in design-,
manufacturing-, and/or operation process the objective is to find and correct failures before
they cause larger problems [4].
Figure 1 illustrates an example of generic worksheet with typical FMEA columns. The
Item column lists up each item which the FMEA is going to focus on. Examples on items
are e.g. hydraulic pump, thrust bearing, gearbox, and furnish burner etc. Function column
lists up what each item is intended to do, sometimes items have many functions and it is
important to describe the function(s) as good as possible. Potential Failure Modes lists up
potential failures that can occur for each item, or every way the item is not functioning
properly decided by the analysis team. There may be more than one and sometimes many
failure modes for each function of an item. Potential Effect(s) of Failure lists up every
possible way a failure can impact the system or end user. It depends on the nature of the
analysis if the team decides to use single description of the effect on the system or three
levels of effect, like: [4]
• Local Effect. The impact the failure has on the item or adjacent items
• Next Higher-Level Effect. The impact the failure has on next higher-level
assembly
• End Effect. The impact a failure has on the top-level system and/or end user
25
Often there are many effects for each failure, but for most applications the analysis team
uses only the ones with most serious effects [4].
Figure 1: Example of generic FMEA worksheet.
Severity column lists up how serious the effects are from a failure. Severity scale can vary
from one analysis to another all depends on the size and complexity of the analysis. Often
there are used five or ten ranking levels for severity, for ten ranking levels the one is least
serious and ten being the most serious and is decided regardless of the likelihood of it
happening or being detected Potential Cause(s) of Failure column lists up the reason for
the failure to occur, which is usually found by the team members constantly asking each
other “why did this happen” until they determine the root cause, often there are more than
one cause for each failure. Occurrence column lists the likelihood that a failure and causes
occur in the item being analyzed, like the severity-scale this is a ranking scale usually from
one to ten [4]. Figure 2 shows typical construction of severity and occurrence scales.
Figure 2: Example of severity and occurrence scales.
Current Design Controls columns list up the methods that are planned or are already in
use to prevent or at least reduce the risk that potential failures cause and prevent
catastrophic brake down. As before often there are more than one control for each cause.
Based on pacific criteria the Detection column lists the ranking number associated with the
Responsible
Person
Actions
Taken
Target
Completion
Date
Effective
Completion
Date
1 2 3 4 5 6 7 8 8 9 10 11
Occ
urr
en
ce Current
Design
Controls
(Prevention)
Current
Design
Controls
(Detection) De
tect
ion
R P
N Recommended
Action(s)Ite
m
Function
Potential
Failure
Mode
Potential
Effect(s)
of Failure Seve
rity Potential
Cause(s)
of Failure
Likelihood
of FailureIncidents per Item Rank Effect Severity of Effect Rank
Very High ≥1 in 10 10Failure mode affects safe operation and/or
does not complies with regulations without warning.10
1 in 20 9Failure mode affects safe operation and/or
does not complies with regulations with warning.9
1 in 50 8Loss of main function
(machine inoperable, does not affect safe operation)8
1 in 100 7Degradation of primary function
(machine operable, at reduced performance)7
1 in 500 6Loss of secondary function
(machine operable but lower level components inoperable).6
1 in 2000 5Degradation of secondary function
(machine operable but lower level components at reduced 5
1 in 10,000 4Appearance and/or audible noise(s)
Noticed by most of the users/operators (>75%).4
1 in 100,000 3Appearance and/or audible noise(s)
Noticed by many of the users/operators (50%).3
1 in 1,000,000 2Appearance and/or audible noise(s)
Noticed by some of the users/operators (<25%).2
Very Low Failure is eliminated 1 No Effect No effects 1
High
Moderate
Low
Failure to Meet
Safety and/or
Regulations
Loss of Main
Functionality
Loss of
Secondary
Functionality
Frustrating
and/or
Annoying
26
most suitable controls from the detection-type controls. The detection ranking considers
the likelihood a failure/cause is detected based on the criteria used for the FMEA. The
criteria can vary based on what type of operation, manufacturing or process is being
analyzed. It is a relative ranking based on the scope of the FMEA and does not considers
the levels of severity and occurrence [4]. Figure 3 shows an example of process FMEA
detection scale and should be tailored depending on the task at hand.
Figure 3: Example of Process FMEA detection scale [5].
Risk Priority Number (RPN) column lists the numeric ranking of the risk the possible
failures can have. It is made up from following three elements:
• Severity of the effect
• Likelihood of occurrence of the cause
• Likelihood of detection of the cause
definition of RPN is descriped as,
𝑅𝑃𝑁 = 𝑆𝑒𝑣𝑒𝑟𝑖𝑡𝑦 ∗ 𝑂𝑐𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑒 ∗ 𝐷𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛 (1)
RPN is not a crucial part of deciding what actions to take against failure modes, they
usually act as threshold values in the evaluation process of these actions. After calculating
the RPN for each failure mode, the ones with the highest RPN should get the highest
priority when deciding appropriate actions. RPN has some limitations and does not
necessary give the right view on the risk involved with a failure mode associated cause.
Recommended Action(s) column lists the appropriate actions the FMEA team suggests
could reduce or prevent the risk associated with each possible cause of failure. These
Opportunity
for Detection
Criteria
Likelihood of Detection by Process ControlRank
Likelihood
of Detection
No Detection
OpportunityNo current process control; cannot detect or is not analyzed. 10
Almost
Impossible
Not Likely to Detect
at any Stage Failure Mode and/or Error (Cause) is not easily detected (e.g., random audits). 9
Very
Remote
Problem Detection
PostprocessingFailure Mode detection postprocessing by operator through visual/tactile/audible means. 8 Remode
Problem Detection
at Source
Failure Mode detection in-station by operator through visual/tactile/audible means or postprocessing
through use of attribute gauging (go/no-go, manual torque check/clicker wrench, etc.).7
Very
Low
Problem Detection
Postprocessing
Failure Mode detection postprocessing by operator through use of variable gauging or in-station by
operator through use of attribute gauging (go/no-go, manual torque check/clicker wrench, etc.).6 Low
Problem Detection
at Source
Failure Mode or Error (Cause) detection in-station by operator through variable gauging or
by automated controls in-station will detect discrepent part and notify operator (light, buzzer, etc.).
Gauging performend on setup and first-piece check (for setup causes only).
5 Moderate
Problem Detection
Postprocessing
Failure Mode detection postprocessing by automated controls that will
detect discrepant part and lock part to prevent further processing.4
Moderately
High
Problem Detection
at Source
Failure Mode detection in-station by automated controls that will detect discrepant part
and automatically lock part in station to prevent further processing.3 High
Error Detection and/or
Problem Preventation
Error (Cause) detection in-station by automated controls that will detect,
error and prevent discrepant part from being made.2
Very
High
Detection Not
Applicable
Error Prevention
Error (Cause) prevention as a result of fixture design, machine design, or part design.
Discrepant part cannot be made because item has been error-proofed by process/product design1
Almost
Certain
27
actions usually consider existing controls, how important the task is, and what is the cost
and effectiveness of the action to the system [4].
Performing FMEA
Procedure to execute a good and inductive FMEA it is crucial that the FMEA team takes
time to evaluate every possible scenario a problem or failure can occur. There is no
standard method for the sequence a FMEA team performs the analysis, however the
following method is widely used [4]:
1. Review the process and list up all the items included in that process.
2. List up all the primary functions each item has.
3. Enter every potential failure mode for each function and corresponding effects.
4. Rank the most serious effects for each failure mode.
5. Enter all the causes for each failure mode and rank them for occurrence.
6. Enter prevention-type controls and detection-type controls for each cause, and
detection-rank the best detection-type control.
7. Analyze each function through RPN.
8. High severity and high RPN functions are reviewed and appropriate actions are
taken to reduce the risk.
9. Review every high risk FMEA issue and every action taken, with management and
next steps decided.
Figure 4 shows the logical relationship between the elements in a FMEA.
Figure 4: The logical relationship between FMEA elements [4].
To sum up, FMEA is a very structured and reliable method for evaluating systems and its
components. The applications and concepts are convenient, and the approach makes
evaluating complex systems more comprehensible. The downside on FMEA is it is often
tedious, time-consuming, and expensive. The approach is not suitable for multiple failure
analysis and human errors can easily be forgotten [6].
28
2.2.2 Root Cause Analysis
Problems or symptoms of problems are often fixed without regards to the actual causes,
which usually leads to recurrence of the same or similar problems.
Root cause analysis (RCA) is a reactive analysis that is carried out after the problem has
occurred. It is a systematic method for identifying root causes of unwanted events or
failures and approach to solve them. Including the identification of the root and
contributory factors, determination, and development of preventive actions along with
measurement strategies to evaluate the effectiveness of these actions. RCA is in theory a
method that uses common sense techniques to produce a systematic, quantified, and
documented approach to identify, understand, and resolve underlying problems [7]. Figure
5 illustrates an example of an RCA 5 step procedure where the steps are:
1. Identify and delimit the problem.
2. Define, describe, and understand the problem.
3. Identify the root cause(s).
4. Suggest and implement required corrective actions.
5. Monitor the system.
Figure 5: Example of a RCA procedure.
There are many methods available for analysis teams to conduct a RCA, depending on
project type, size, and complexity teams decide what method suits them. To name few
common ones:
• Brainstorming
• The “Five Whys”
• Cause & Effect Diagrams
• Fault Tree
A brief introduction on these methods will be in the following sections, in section 2.2.3
Fault Tree Analysis will be discussed.
29
Brainstorming
Brainstorming is a well-known strategy for problem solving certain types of problems that
occur time and a time within companies. It is a strategy based on a group of people
meeting and exchanging ideas to try to solve the problem. There are some pointers for a
good brainstorming session, which are: [7]
• Collect as many ideas as possible from all team members, without criticism or
judgement when ideas are being born.
• Welcome every idea, no matter how silly they may seem.
• Secondary discussions should not take place during the brainstorming.
• Build on every possible idea born.
• Document all ideas.
• After brainstorming, discuss ideas and possibility for solving the problem.
Typically brainstorming is an early stage procedure to try to understand and solve the
problems at hand. When problems become more complex there is need for more powerful
methods.
The “Five-Whys”
The Five-Whys strategy typically refers to the practice of asking why the failure occurred
five time in order to get to the root cause(s) of the problem. It is a method and does not
require any special technique to perform [7]. Figure 6 illustrates an example of a problem
and solution using the Five-Why method.
Figure 6: Example of a "Five-Whys" problem and solution.
Cause & Effect Diagrams
Cause & effect diagrams (CED), also known as Fishbone diagrams or Ishikawa diagrams,
is a useful technique to perform a more complex RCA. This type of diagram is powerful to
identify all potential contributing processes and factors to the problem. It is performed by
successively asking what effects have occurred and why, and then proceed backwards from
last failure to find the cause. It starts with the most significant event and determining the
cause(s) of it, then the cause(s) for this event’s cause(s) are determined. This chain of
events and causes is continued until no other causes are determined, these causes are
30
verified by determining if the criteria for the root cause have been met [8]. Figure 7
illustrates how the Cause and Effects diagram is constructed, also how its structure looks
like a skeleton of a fish, hence the name Fishbone diagram.
Figure 7: Example of the Cause and Effect Diagram.
2.2.3 Fault Tree Analysis
Fault tree analysis (FTA) is an analytical technique widely used in vast varieties of
industry all over the world. Where the goal is to specify undesired states of a system,
normally a state where safety is compromised, then analyze the system in context with its
environment and operation. This is performed to locate or find every way were these
unusual events can occur. The method uses graphical modeling with vast varieties of
parallel and sequential combinations of faults that can occur. Events associated with
components failing, human errors or any other sort of event represent a fault in the system.
The fault tree depicts all basic events and their relations which ends up with undesired
condition on the system [9].
FTA uses Boolean logic to analyze a fault with combining effects of lower level events,
usually to determine the probability of a complex safety hazard. This is performed to
develop actions to reduce or possibly remove any safety concerns existing in the system.
Usually FTA is applied when the consequences from a failure are great and safety
hazardous and the failure is complex with many smaller events contributing to the event
[4].
FTA & FMEA Differences
There is some fundamental difference between FTA and FMEA, the main difference is that
an FTA is a deductive analysis but FMEA is inductive analysis. In short term, deductive
methods are top-down methods that aim to analyze the effects an initializing fault or event
31
has on a system and analyze it to its root cause. Inductive methods are bottom-up analysis
which aim to analyze the effects a single component or part failing has on the system or
sub-systems [9]. Amongst other differences include:
• FTA represents a graphical solution of complex relationships in the system that
lead to failure, when FMEA uses worksheet to represent its solution.
• FTA recognizes any interactions between failures and every sub-level event that
contribute to the failures, also it considers if two or more sub-level events must be
present for the failure to occur. When FMEA normally just considers each
contributor separately.
• FTA is capable to incorporate the probabilities of each of the sub-level events and
their complex relations to the failure. FMEA does not normally support these
probability calculations of the failure. [4]
FTA is a powerful method when it is crucial to understand all interconnected relationships
between top event, usually some type of fault, and their causes and effects. It is common
that a FMEA team uses FTA when the failure mode gets complex with many causes and if
two or more causes are not unique and occur side by side. By doing that the team gets a
better visual image on the problem’s complex nature.
FTA Events and Gates
In FTA the symbols events and gates are used to represent the logic of the analysis, they do
not necessary correlate to the systems components or part. Fault or another unwanted
occurrence is represented both graphicly and mathematically as an event. Each event is
associated with a probability of occurrence or a distribution function. There are multiple
types of events used in FTA and it depends on the complexity of the system how many
types needed for the analysis, but the most common events are: [4]
• Top-Level Event is the event that the FTA focuses mostly on, every gate or sub-
level events lead up to this event.
• Intermediate Event is a resulting fault because of one or more antecedent causes.
• Basic Event is initiating fault that needs no further development.
• Undeveloped Event is event that is not developed further.
• Conditioning Event is some special condition that applies to one or more gates.
• External Event is an event that usually is expected to occur or not.
Gate is a logical symbol used to display interconnecting events and conditions in the fault
tree. There are two basic types of fault tree gates which are AND-gate and OR-gate, these
gates are most common ones used for the FTA, all other types of gates are simply special
cases of those two. The number of inputs to the logical gates are not limited and sometimes
the inputs are multiple for various reasons. The logical gates used in FTA are: [4]
• AND-Gate, where the output event occurs only if all input events occur.
• OR-Gate, where the output event occurs if at least one input event occurs.
• Voting OR-Gate, where the output event occurs if at least certain number of input
events occur.
• Priority AND-Gate, where the output event occurs only if all the input events
occur in a specific order.
32
• Exclusive OR-Gate, where the output event only occurs if exactly one of the input
events occurs.
• Inhibit-Gate, where input event occurs if all input events occur and other
conditional event occurs.
Figure 8 shows example of FTA symbols used for events and gates, even though there is
no universally standardized way to represent them, this is a widely used method.
Figure 8: Example of fault tree event and gate symbols.
FTA Construction
When constructing a fault tree analysis, the method requires input information from the
system being analyzed. Often this information is gained using known failure mode
methods like FMEA or any other recognized strategy that gives deeper understanding on
the system and its complexity. Commonly the inputs to the fault tree are entered as a
probability value, current or expected, of the respective root node. These probability values
are often calculated reliability (R) values or failure-probability (FP) values, where,
𝐹𝑃 = 1 − 𝑅 (2)
Common method for estimating the statistical probability of a failure is based on the
exponential and Weibull distributions, but other methods are also known to be used.
Equation (3) shows the method that uses exponential distribution and assumes the failure
time to be constant. Where (λ) is the inverse of statistically relevant mean time to failure
(MTTF) of the root node, and (t) can represent e.g. running hours of the component
connected to the node event. The time value can easily be replaced by any relevant
parameter that effects the reliability of the system or any of its components [10].
𝐹𝑃𝑒𝑥𝑝 = 1 − 𝑒−𝜆𝑡 (3)
Primary Event Block Classic FTA Symbol Name of Gate Classic FTA Symbol
Basic Event AND
Intermediate Event OR
External Event Voting OR
Undeveloped Event Priority AND
Conditioning Event Exlusive OR
Transfer Inhibit
Fault Tree Symbols
Event Symbols Gate Symbols
33
Weibull distribution is the most widely used distribution type, it has been applied
successfully for years for modeling lifetime calculations for various equipment e.g.
bearings, jet engines, and composite materials. The Weibull distribution consists of two or
three parameters, shape-, nominal life-, and minimum life parameter, which makes it
versatile. Often the minimum lifetime parameter is not used when calculating failure
probability, Equation (4) gives the two parameter version, where (η) is the nominal lifetime
parameter and (β) is the shape factor [10].
𝐹𝑃𝑤𝑒𝑖𝑏 = 1 − 𝑒
−(𝑡𝜂
)𝛽
(4)
Commonly the first guess for η is a value close to the MTTF value of representing
component. Failure occurrence type and the status of the system decides the shape factor β
where the general rule is, [10]
• If β < 1, The system is at its infant mortality stage, where the failure rate decreases
as a function of time
• If β = 1, The system is at its useful service life, where the failure rate is a constant.
• If β > 1, The system is at its wear out period, where the failure rate starts to
increase as a function of time.
Utilizing data from maintenance database and real time monitoring methods, e.g. vibration-
and lubrication measurements, is very effective strategy to get a good indication on the
failure probability. This is very important since statistical modeling on the failure process
and the wear out period is often inaccurate. As described earlier, the failure probability at
each node of the fault tree is calculated with a method based on Boolean algebra, equations
(5) and (6) show the calculation rules for AND & OR gates when the inputs are failure
possibility values [10].
𝐹𝑃𝑂𝑈𝑇𝐴𝑁𝐷= 𝐹𝑃1 ∗ 𝐹𝑃2 … ∗ 𝐹𝑃𝑛 (5)
𝐹𝑃𝑂𝑈𝑇𝑂𝑅= 1 − (1 − 𝐹𝑃1) ∗ (1 − 𝐹𝑃2) ∗ … ∗ (1 − 𝐹𝑃𝑛) (6)
Example on a hydraulic system is illustrated in Figure 9 and its corresponding fault tree.
The system is made up with two hydraulic pumps driven by two electric motors and a
generator supplying electricity. The pumps supply a flow of hydraulic fluid through the
valve for further use. Here the focus is the failure in which is if there is no flow of
hydraulic fluid from the valve, defined as the top event. Next step is to recognize what
could cause this, first the valve could be closed or blocked for some reason, secondly there
could be no flow from either of the pumps and finally there could be a power loss to the
system, since there is one generator supplying both electric motors. These events are
defined as intermediate events and are connected to the top event with OR-gate, since each
one of these events could cause the top event to happen. Next step is to look at each of
these intermediate events and consider, what could cause them to happen. The valve is not
a complex component and requires no further analysis. The generator is a complicated
mechanism and would need much deeper analysis to find the basic events that cause him to
not work, but to keep the complexity level down the generator will not be analyzed further
in this example. The event when there is no flow from the pumps must be considered
further and since there are two pumps, they must both fail so that this event can occur. Two
new intermediate events are created, and they are if there was No hydraulic fluid from
34
pump 1 and No hydraulic fluid from pump 2. These events are connected to the event No
hydraulic fluid from the pumps with AND-gate because they must both occur at the same
time for that event to happen. The intermediate events No hydraulic fluid from pump 1 and
pump2 are each broken down into two basic events, Failure of pump and Failure of motor
which are connected with OR-gate, since either one of these basic events can cause there
was no hydraulic fluid from pump 1 or pump 2.
Figure 9: Example of hydrolic system and corresponding fault tree.
This is an example version of a fault tree but in practice, the FTA team would need to
analyze the system much deeper to get acceptable results.
To sum up, FTA is a powerful tool to identify all possible causes of a specified undesired
event, known as Top Event. FTA is structured as a Top-to-Bottom method, or deductive
analysis. Utilizing FTA leads to deeper knowledge of system characteristics and design
flaws and insufficient operational and maintenance procedures may be reveled and
corrected during the construction of the fault tree. The downside is that FTA is not fully
suitable for modeling dynamic scenarios. FTA is a binary method and therefore it is
possible it fails to address some problems [6].
2.3 Condition Monitoring
Condition monitoring of machinery is when various parameters related to the mechanical
condition of the machinery are measured. These parameters (like vibration, temperature,
lubricant condition, and performance), help make it possible to determine if the machinery
is in good or bad condition. When these parameters indicate that the mechanical condition
is bad, condition monitoring makes it possible to determine the problems cause [11].
There are countless companies and factories in the world that operate constantly all year
around. To prevent unforeseen stop or breakdown on machinery, it is important to monitor
machinery condition. Condition monitoring is a crucial aspect of running a constant
operation, were it is possible to monitor current condition and predict the future condition
35
while machines are in operation. To obtain information regarding the condition, internal
information must be extracted externally while machines are in operation. There are two
main techniques, Vibration- and Lubricant analysis, used to extract this information,
there are other methods also used e.g. Performance- and Thermography analysis [1].
Condition monitoring systems are categorized into two types, Periodic- and Permanent
Monitoring [1].
Periodic Monitoring, where machines condition parameter(s) are measured, then
analyzed, at some certain time interval. The intervals must be sufficiently shorter than the
minimum required lead times for maintenance and production planning purposes. For fault
detection and diagnosis, advanced digital processing techniques are usually needed (see
chapter 4 for further discussion). Periodic monitoring often provides early stage
information about incipient failure and is usually used where, early warning is required,
advanced diagnostics are required, machine has many measurement points, and machines
are complex. Periodic monitoring has some advantages and disadvantages, they are:
• Advantages, Insignificant cost of monitoring equipment and potentially provides
much more advance warning on impending failures.
• Disadvantages, Unforeseen rapid failures could be missed, which lead to a
catastrophic breakdown.
Permanent Monitoring, where machines condition parameter(s) are measured constantly,
at some fixed point or place, and the measurements are compared to acceptable levels
(often some type of standard like ISO). Permanent monitoring is usually used when
machines must be monitored constantly, and to provide a warning if operational condition
worsens or to shut down the machine when preset limit is exceeded. This is used where
failure on a machine can cause a catastrophic failure to production or surrounding
equipment. In a permanent monitoring system, all transducers are fixed to certain
measuring points, often decided by the manufacturer of the machine or appropriating
standard. Permanent monitoring has some advantages and disadvantages, they are:
• Advantages, it has a quick response to sudden change in operational conditions
and therefore protects the equipment best way possible for unforeseen failures.
• Disadvantages, the cost is very high and usually it is only applied to the most
crucial machines and the most expensive ones. Since the response must be fast it
usually monitors noncomplex parameters, like RMS- or Peak-Values (see further in
section 3.1.1). This results in poorer capability to perform advance vibration
analysis on impending failures, meaning that the warning goes from weeks or
months to days or even hours.
But it must be kept in mind that even if transducers are permanently mounted to a machine,
it is still possible to perform advanced analysis on the vibration signals, just not
continuously. This gives the advantage that periodic monitoring is carried out alongside the
permanent monitoring, giving the possibility to perform measurement at much more
frequent intervals [1].
36
In the following sections the subjects listed below will be discussed, they are:
• Vibration Measurements and Transducers
• Lubricant Analysis
• Performance Analysis
• Thermography
Where the focus will be on the vibration section, although the other subjects will get some
brief introduction.
2.3.1 Vibration Measurements and Transducers
Every rotating machinery has one or more rotating machine elements that turn with the
drive shaft. These elements are e.g. rolling element bearings, impellers, and any other type
of rotors. If, in theory, a machine was perfectly balanced all rotors would rotate perfectly
around their centerline and all acting forces would be equal, hence no vibration. In
practice, every rotating machine generates vibration when in operation, even though it’s in
perfect condition. Often this vibration is directly linked to periodic events like rotating
shaft or impeller. These periodic events generate vibration at some certain frequencies,
thus multiple diagnostic techniques have been developed that are based on frequency
analysis. Not every vibration generated in machinery is based on its shaft rotation e.g.
internal combustion engines have fixed number of combustions every engine cycle. Some
vibration can also be linked to fluid flow in e.g. blowers, turbines, fans, compressors, and
pumps, this kind of vibration often has unique characteristics linked to the housing and
impeller geometry.
Vibration Measurements
For machines vibration measurement, some technical equipment is needed. Various
equipment is used in practice, all from instruments that measure overall vibration to
complex multichannel analyzers with numerous features for analyzing the measurement
data. A scheme of a vibration measurement and analysis system is illustrated in Figure 10.
Figure 10: Scheme of a vibration measurement and analysis system.
Vibration measurements are divided into two main categories, Absolute Vibration and
Relative Vibration. Where absolute vibration is the absolute motion of the bearing housing
or any other component being measured. When relative vibration is defined as the relative
motion between a shaft and its casing or bearing house.
37
Absolute vibration measurements are the most common ones in condition monitoring, and
usually performed with accelerometers. This type of measurement gives more possibilities
to perform more deeper analysis on the machines condition
Relative vibration measurements are often used on large machines that use journal-
bearings, since the relative motion of the shaft is closely related to the oil film thickness in
the bearing. Information about the oil thickness are very useful for rotor dynamics
calculations [1]. Comparison between absolute measurement and relative measurement is
illustrated in Figure 11.
Figure 11: Comparison between Absolute- and Relative Measurements.
Vibration Transducers
To measure vibration in machinery or other structure, a vibration transducer is used. A
transducer is a device that converts the mechanical motion (vibration) into equivalent
electrical signal. There are three parameters in which lateral vibration is expressed, they
are displacement, velocity, and acceleration. Commonly these transducers are called,
proximity probes, velocity transducer, and accelerometers. Each type of transducer has
distinct advantages for certain applications, they also have some limitations, so no single
type of transducer satisfies all measurement needs. So, it is important to consider what type
of transducer is best suited for each job [3].
Proximity Probes
Proximity probes, also known as Eddy current transducers, are the favored transducers for
shaft vibration monitoring on large machinery equipped with journal bearings, e.g. steam
generators in a powerplant. Proximity probes are the only transducers that offer
measurement on shafts relative motion against bearing housing or casing. Most bearing
houses have a horizontal split, especially large bearings, thus the proximity probes are
mounted at 45° angle on both sides of the vertical plane. It is extremely important that the
probes are mounted perpendicular to the shaft centerline, deviation by more than 1-2° will
affect the output sensitivity.
38
There are few methods commonly used for installation of proximity probes, which include
internal, internal/external, and external mounting. During internal mounting the proximity
probes are mounted inside the bearing housing with brackets. The transducers are installed
and gapped properly before the bearing cover is installed again, holes are drilled in housing
for transducers cables. During external/internal mounting a mounting adaptor is used,
giving external access to the proximity probe, but the tip of the probe is inside the bearing.
During external mounting, a bracket is fastened on the outside of the bearing housing and
the proximity probe is mounted on that bracket. This is usually last resort installation,
because this method commonly gives less accuracy due to interference [3]. The difference
between these methods is illustrated in Figure 12.
Figure 12: Comparison between mounting setups for proximity probes.
Proximity probe measurement system consist of the probe, extension cable, and a
‘proximitor’ (oscillator/demodulator). The medium in the gap must have a high dielectric
value, often the medium consists of air or another gas and oil in e.g. journal bearings. It is
essential that the surface whose distance from the tip of the probe is being measured is
electrically conducting, to allow eddy currents to be generated by induction. The
‘proximitor’ generates a high frequency signal where its amplitude is directly dependent on
the distance between the surface and the tip of the probe. The cable is manufactured under
strict tolerances, regarding electrical values, and its length cannot be altered with. If a cable
gets damaged, or its shield, it threatens the measurement quality.
A typical proximity probe can measure linearly a gap range from 0.25 to 2.3 mm with
highest deviation from linearity of 0.025 mm, about 1.1% of full scale with a sensitivity of
7.87 V/mm. The ratio of maximum to minimum value gives a dynamic range less than 20
dB, but the ratio of the maximum to minimum component in a spectrum is limited by the
nonlinearity, at best 40 dB [1].
Linearity is not the biggest limiting factor for the dynamic range of the measurement. The
biggest limitation factor is because of so called runout. Runout is defined as the signal
measured in the absence of actual vibration and is composed of mechanical- and electrical
runout. Mechanical runout is caused because of mechanical deviations of the shaft surface
from a true circle, concentric with the rotation axis. These include low-frequency
39
components as eccentricity, shaft bow and out-of-roundness, and short components like
scratches, burrs, and other local surface damage. Electrical runout is caused by variations
in the local surface electrical and magnetic properties, often caused by residual magnetism,
residual- or local stresses in the material as well as imperfection in the subsurface of the
material [1].
The valid frequency range for proximity probes is usually about 10 kHz, but this can often
be misleading, as the true limit is usually given by certain number of shaft speed
harmonics, this is caused by the dynamic range limitation. Rarely the range goes over the
10th harmonic, due to runout restrictions. Because of these restrictions, proximity probes
diagnostics capability is limited, especially for diagnosing imminent failures with long-
term advance [1]. A scheme of proximity probe system, based on Eddy currents, is
illustrated in Figure 13.
Figure 13: Scheme of a Eddy current based proximity probe system.
Velocity Transducers
The velocity transducer, like the name indicates, is a measuring device that measures the
velocity of a vibrating body. It is widely used for vibration monitoring of rotating
machinery. This type easily installs on most common analyzers and is rather inexpensive,
compared to other types of transducers. It is ideal for general purpose machine monitoring
applications and is available in many different physical configurations and output
sensitivities. These transducers are electromechanical sensors and typically have a
seismically suspended coil in the magnetic field of a permanent magnet. Usually the
permanent magnet is mounted fixed to the housing of the transducer, but it is also known
where the coil is mounted fixed to the housing and the magnet is seismically suspended. It
is said that a mass is seismically suspended when it is attached to a body with a spring.
When the body vibrates the mass moves with it at low frequencies, but when the vibration
exceeds the natural frequency of the mass on its spring, the mass will remain fixed in
space, and the body moves around it [1]. In Figure 14 the two basic types of velocity
transducers are illustrated, often referred to as magnet-in-coil and coil-in-magnet.
Theory of operation is when a coil moves in magnetic field, or the magnetic field moves
near a coil, a voltage is induced in the end wires of the coil. As the coil, or magnet, is
forced to move by the vibrating motion of the housing, a voltage signal correlating with the
vibration is produced. The velocity transducer does not require any external devices to
produce voltage signal, it is a self-generating sensor, and the voltage generated by the
transducer is in direct proportion to the velocity of the motion [3].
40
Figure 14: Two basic types of velocity transducers, Magnet-In-Coil and Coil-In-Magnet.
Velocity transducers are sensitive to gravity and therefore their manufactured differently
for vertical or horizontal axis mounting. Because of this sensitivity a caution must be taken
when mounting them to a rotating machinery. Velocity transducers are also susceptible to
cross-axis vibration, that can damage their functionality and give wrong measurements.
Usable frequency range of the velocity transducer is determent by mechanical parameters
of the components. These parameters are, spring stiffness, material damping, and the
weight of the coil/magnet, which determine the low frequency respond of the transducer.
The resonant frequency of the transducer is usually below 10 Hz and the range are often
between 10 Hz and 1000 Hz [8].
Accelerometers
Accelerometers are the most common transducers used for vibration measurements on
rotating machinery. They have many advantages like, lightweight structure, compact and
ruggedly build, and they have a wide frequency response range. Disadvantages are also
very few, like they cannot measure at 0 Hz. These transducers are very commonly used in
condition monitoring on all kinds of machinery [3].
Piezoelectric accelerometers are the most common used in machine condition monitoring.
These sensors use the piezoelectric properties of some solid materials, often some certain
type of crystals or ceramics. When piezoelectric materials undergo deformation, they
generate electric charge that is proportional to strain. The three main design types of
piezoelectric accelerometers are illustrated in Figure 15, they are, compression-, shear-,
and bending-type.
Figure 15: Three main types of accelerometers, Compression, Bending, and Shear.
41
Figure 16 shows the design of a typical compression type accelerometer where a
piezoelectric element is compressed between a seismic mass and the base, forming a
sensing element. The seismic mass is flexibly supported, where the pre-stressed screw with
piezoelectric element act like a very stiff spring. Resulting in a large stiffness to mass ratio
and therefore very high resonant frequency of the sensor and guaranty of a linear behavior.
Figure 16:Typical compression type piezoelectric accelerometer with top connection.
When an accelerometer is mounted on a vibrating body, the seismic mass and the
piezoelectric materials are forced to move with the vibrating body. The vibrating body
causes the seismic mass to apply force on the piezoelectric elements resulting in slight
deformation, giving a strain proportional to the variation in acceleration. This strain on the
piezoelectric elements causes them to produce electrical charge proportional to the
acceleration of the vibrating body. The electric charge produced by the piezoelectric
elements is quoted in picocoulombs per meter per second squared, pC/(m/s2), often
referred to as the sensitivity of the accelerometer. Electric charge is a quantity that cannot
be transmitted over long distances, that’s why modern types of accelerometers have a build
in pre-amplifier. The pre-amplifier converts the electric charge into voltage signal,
common design converts 1 pC to 1 mV, then the sensitivity of the sensor is expressed in
mV/g [1].
The design of the accelerometer allows it to be attached in any orientation to a machine.
The stiffness of the mass/spring system is high enough so that the orientation does not
affect the measurements (unlike the velocity transducer). However, it is crucial that the
piezoelectric element inside the sensor is not exposed to other load than from vibration.
Therefore, it is essential that the surface that the sensor is to be mounted on is smooth and
flat to prevent any deformation of the sensor’s base. Other factors that can cause a
deformation of the base and cause a faulty measurement are, temperature changes and
excessive torque applied to mounting screws. The compression type accelerometer is the
most prone to these factors, but the other two types are much more resistive to them. Each
types advantages and disadvantages are listed in Table 1 [12].
42
Table 1: Accelerometer advantages and disadvantages, comparison between types.
Modern shear type accelerometers are produced as, Delta-Shear accelerometers, it draws it
name from that there are three piezoelectric elements arranged in a triangle and oriented so
that the effects from base deformation are minimized. This type of accelerometer is quite
sensitive and durable and does not have the disadvantages of the compression type. It has
become the most common type of accelerometer used for machines absolute vibration
measurements [12].
Since accelerometers do not contain any moving parts, they are very durable and reliable,
and they do not need to be calibrated frequently, like the velocity transducer. They are
mounted easily onto machines and they can be used where wide frequency range is
required (0.1 Hz to 30 kHz) and they have large dynamic range, they are also available
with high temperature shield [12].
There are several ways to mount accelerometer to a machine, e.g. using a screw or stud,
glue or other adhesive, magnet, and probe, just to name few. It is important to realize that
the sensor only measures what is happening to itself. Keeping that in mind, attachment
method should be chosen that ensures that the sensor measures the same that is happening
to the machine. Inappropriate attachment degrades the measured data and reduce usable
frequency range of the sensor; therefore, attachment must be chosen accordingly to the
frequency range of interest [12]. This is described in detail in ISO-5348 standard. Figure
17 illustrates very common mounting methods and how the frequency range is affected by
the chosen method.
Figure 17: Methods for mounting accelerometers.
Accelerometer Type Advantages Disadvantages
Low sensitivity
Sensitive for temperature effects
Sensitive for base deformation
Fragile and shock sensitive
Shear Type
Compression Type
Bending Type
Wide frequency range
Quite Durable
Low temperature influence
Wide frequency range
Durable to shocks
Very low frequency measurements
Very high sensitivity
43
Accelerometer cabling is an important part of the measurement chain, through the cabling
the signal from the sensor is transmitted to the analyzing equipment. The electric circuit in
the accelerometer, including the piezoelectric elements, has very high impedance and is
subject to some problems, like pickup of signals from electromagnetic radiation. This is
minimized by using coaxial cables with outer braided wire shield, but it is important that
the wire shield is grounded at only one end of the cable. The best cable connection for best
measuring results is the microdot connector, often used for laboratory measurements.
There are many configurations available on the market that are more convenient to use out
in the field, but it is crucial that the person performing the measurements is aware of the
environment conditions that can affect the measurement results [1].
2.3.2 Oil/Lubricant Analysis
Lubricant- or oil analysis has become increasingly more popular through the years when
companies are conducting a predictive maintenance strategy. The method has evolved
greatly through the years and has become a reliable source for condition monitoring. Oil
analysis has become the second most used method in condition monitoring, after vibration
analysis. It is not merely a tool to analyze the state of the lubricant itself, with the latest
diagnostic tools, it is used as a reliable tool to evaluate the condition of machinery. When
maintenance teams utilize these advanced techniques the reliability of the equipment
increases, and unforeseen failures and downtime are minimized [3].
There are various wear mechanisms that deteriorate machines components, even though
there are many different types of wear, there are just few primary sources resulting in wear.
The mechanisms that contribute to the wear of a component include e.g. misalignment,
unbalance, overload, or excessive heating conditions. Often the lubricant itself is the
source for the wear, e.g. when the lubricant has degraded or become contaminated. Types
of wear that can occur in a machine are: [3]
• Abrasive wear
• Adhesive wear
• Cavitation
• Corrosive wear
• Cutting wear
• Fatigue wear
• Sliding wear
When machines are in operation the lubricant normally carry the debris away. Analysis and
identification of this wear debris can recognize its type and identify the source. Lubricant
analysis can underline the necessity to perform a corrective action to prevent imminent
stops or breakdowns. There are known cases where oil analysis identified defects in a
rotating machinery even before a vibration analysis could detect it. This applies especially
to large slow speed machines with high load level, e.g. diesel engines. For those reasons,
oil analysis has become an important predictive maintenance technique [3].
To implement a condition monitoring program, based on oil analysis, it is important to use
proper tests that will identify unusual wear particles in the oil. Each program must be
customized to the type of equipment being used and monitored, and what types of failures
to be expected. The sampling locations, types of tests, and interpretation of oil analysis
44
depend greatly on what type of machinery is being monitored, e.g. compressor, steam
turbine, diesel engine, gearbox or a hydraulic system [3].
The prime indicators of the machine’s condition or its health are the wear particles in the
oil. There are many known techniques to evaluate the type and concentration of these
particles, these techniques include, spectrometric analysis, particle counting, direct reading
ferrography, and analytical ferrography. It is necessary to trend the condition of the oil
itself, in addition to the wear particle analysis. The condition of the oil has great impact on
how much wear is generated in a machine, good condition reduces generation of wear and
bad condition increases generation of wear. Thus, the analysis of oil condition is a crucial
part of the program, one can argue it is a proactive maintenance program.
Oil analysis is primarily a combination of two types of analyses, where one is of the
lubricant itself and the other one is analyzing the contaminants in the lubricant. Well
known types of oil analysis are: [3]
• Viscosity measurement
• Solids content
• Water content
• Total acid number & Total base number
• Flash point measurement
A detail literature about oil analysis is reviewed in chapter 7 in [3].
2.3.3 Thermography
Infrared thermography is a method based on using a thermal imager to detect heat that is
emitted by an machines component(s). This technique allows the maintenance team to
validate normal operational conditions, and more importantly, locate thermal anomalies
which possibly indicate abnormal condition or faults. Thermo diagnostics is widely known
method used in various industrial situations, including:
• Electrical systems, to locate faulty connections or overloaded circuits.
• Mechanical equipment, to locate hotspots on motors or faulty bearings.
• Fluid systems, to locate line blockage, verify tank level or pipe temperature.
Faulty state of machines components often results in an increase in temperature. Figure 18
illustrates three images of faulty components, first one shows a faulty motor bearing, the
second shows a motor coupling overheating because of misalignment or similar problem,
and the third one shows a faulty journal bearing [13].
Figure 18: Thermal images of faulty components.
45
2.3.4 Performance Analysis
Performance analysis is based on monitoring crucial operational parameters to evaluate
operational condition of a machine. For certain types of machines, a performance analysis
is an effective way to determine if machine is functioning properly. One example where
this method is widely used is in the powerplant industry. Where gas- and steam turbine
engines are permanently mounted with numerus transducers, for e.g. temperature-,
pressure-, and flowrate measurements. Using the information from these transducers it is
possible to calculate various efficiencies and compare them to the normal condition [1].
47
3 Vibration Signals
A basic understanding on how a vibrating system work and respond to external forces is
very helpful when conducting a vibration analysis. In this chapter, definition of vibration
and its classification will be introduced. Vibrating signals from rotating machinery will be
discussed, where the focus will be on machinery made up with rotating shaft, impeller, and
rolling element bearings.
3.1 Theory and Classification
Any motion of a mass, body, or structure that repeats itself after some interval of time is
called vibration or oscillation. Well known examples of vibration are e.g. a swinging
pendulum or the motion of a plucked guitar string.
3.1.1 Vibration Theory
Vibration theory covers the study of oscillatory motions of bodies and the forces associated
with them. In general, a vibrating system includes a means for storing kinetic- and
potential energy, and a means by which energy is gradually lost. A vibrating system
involves the alternately transfer between potential and kinetic energy, and if the system is
damped, the energy is gradually dissipated in each cycle of vibration. If a damped vibrating
system is to maintain its vibration, an external force must be applied to the system to
maintain the vibration. In practice every vibrating system is a damped one.
Mass-Spring-Damper System
A solid understanding of how a mass-spring-damper system responds to an external force
can contribute in understanding, recognizing, and solving problems encountered in
vibration analysis. Figure 19 shows an example of a mass-spring-damper system, where a
mass M is attached to a spring with a stiffness k and on the other side the mass is attached
to a damper with a damping coefficient c.
When an external force F acts on the mass M and moves it forward the spring gets
stretched and the dampers piston moves forward inside the dampers cylinder. The acting
force F must overcome three things, the mass inertia, the spring stiffness, and the
resistance of moving the dampers piston.
Figure 19: Example of a Mass-Spring-Damper System.
48
In practice every machine has three fundamental properties, that together determine how
the machine responds to external forces that cause vibration, just like the mass-spring-
damper system. These properties represent the inherent characteristics of a machine with
which it will resist vibration. These fundamental properties are: [3]
• Mass, M: Represents the inertia of a body to remain in its neutral state of rest or
motion. External forces try to change the state of rest, which is resisted by the mass
of the body. Mass is measured in kilograms [kg].
• Stiffness, k: To bend or deflect a structure certain distance, a certain force is
required. Structures stiffness is a measure of the force required to obtain a certain
deflection. Stiffness is measured in newtons per meter [N/m].
• Damping, c: When an external force sets a structure into motion, the structure has
inherent mechanisms to slow down the motion’s velocity. The ability to reduce the
motion’s velocity is called damping. Damping is measured in newton seconds per
meter [N s/m or N/(m/s)].
The combined effects of a systems mass, stiffness, and damping determine how that
system responds to the effect of external forces. A defect in a machine often results in
increased vibration. The mass, stiffness, and damping of the system try to oppose the
vibration caused by the defect. If the vibration caused by the defect is larger than the net
sum of the three restraining characteristics, the resulting vibration will be higher and the
defect is detected [3].
Vibration Characteristics
By noting the vibration characteristics of a machine, many useful information about its
condition and possible mechanical problems are obtained. By plotting the movement of a
mass with respect to time, in a mass-spring system, it is possible to study the vibration
characteristics. Example of such system is illustrated in Figure 20, where one cycle of
motion takes 4 seconds, hence the frequency f is 0.25 Hz (where f = 1/(cycle or period)).
The motion of the mass from its neutral position, to the top position, back to neutral
position, to the bottom position, and finally returning to the neutral position, represents one
cycle of motion. From this single cycle of motion, all the information needed to measure
the system vibration are obtained. If the motion continues the cycle simply repeats itself.
Figure 20: Harmonic cycle, locus of mass-spring motion with respect to time.
49
This type of motion is called periodic and harmonic, where the relationship between the
displacement of the mass and time are expressed with the following sinusoidal equation,
𝑥(𝑡) = 𝑋0 ∗ sin(𝜔𝑡) (7)
Where x is the displacement, usually stated in micrometers [µm], at any given instant, X0 is
the maximum displacement of the mass, ω is the angular velocity (where ω = 2π*f), and t
represents the time.
When the mass moves up and down, its velocity changes from zero to maximum. The
velocity, usually stated in millimeters per second [mm/s], is the first derivative of the
displacement with respect to time, hence the velocity of the mass is expressed with:
𝑣(𝑡) =
𝑑𝑋
𝑑𝑡= 𝑋0 ∗ 𝜔 ∗ cos(𝜔𝑡) (8)
Since the mass’s velocity is varying, its acceleration, usually stated in meters per second
squared [m/s2] or in g’s, also varies and is obtained by differentiating the equation for
velocity, therefore the acceleration is expressed as:
𝑎(𝑡) =
𝑑2𝑋
𝑑𝑡2= −𝑋0 ∗ 𝜔2 ∗ sin(𝜔𝑡) (9)
Equations (7), (8), and (9) show the mathematical relationships between displacement,
velocity, and acceleration. These relationships show that if the displacement is harmonic,
the velocity and the acceleration are also harmonic, but the phase shifts. From this
perspective, it does not matter which variable is chosen to describe the vibrational
behavior, it is just a matter of scale and the phase shift. Figure 21 illustrates how the scale
and phase relationships are between displacement, velocity, and acceleration when the
maximum displacement X0 equals to 1 mm and angular velocity ω is equal to 2 rad/s.
Figure 21: Relations between displacement, velocity, and acceleration.
Even though there is a clear relation between acceleration, velocity, and displacement it
should also be considered adverse factors that affect the accuracy of the measurement. Its
therefore advisable to choose appropriate way that suits the given task, to give sufficient
signal to noise ratio. In practice, signal noise is always present and when dealing with
50
weak signals it is important to use the right way, since measurement errors increase with
higher noise.
It is important to look at what frequency range machine operates on when choosing
between displacement, velocity, or acceleration. Commonly velocity is used in the
frequency range 10 Hz to 1000 Hz, acceleration is preferred for higher frequencies, and
displacement is usually used for low frequencies [12].
Vibration signal representation
Vibration signals, often referred to as vibration spectrum, are displayed in two main ways,
that is in time domain or in frequency domain. Time domain plot presents the amplitude on
the vertical axis and corresponding time on the horizontal axis. Time domain spectrum is
the sum of all contributing vibration components that are present in the signal. Frequency
domain plot presents the amplitude on the vertical axis and corresponding frequencies on
the horizontal axis. With mathematical techniques it is possible to convert vibration
spectrum from time domain into frequency domain, such methods are discussed further in
chapter 4. Example of a vibration spectrum, measured from a centrifugal blower, is
illustrated in Figure 22, where the spectrum is displayed both in time domain and
frequency domain.
Figure 22: Vibration spectrum displayed in time domain and frequency domain.
To describe vibrations signals, various characteristics are often used, rather than amplitude,
namely, peak value, RMS value, average value, or peak-to-peak value. Where peak value
represents maximum displacement X0 (equals the amplitude for harmonic signals), hence
the peak-to-peak equals two times the maximum displacement. Single harmonic wave is
illustrated in Figure 23 displaying the comparison between these representations.
Figure 23: Amplitudes comparison for a single harmonic wave.
51
The average value, which is not used often in practice, is determent by following equation,
where T is the time period,
𝑥𝐴𝑣𝑒𝑟𝑎𝑔𝑒 =
1
𝑇∫ |𝑥(𝑡)|
𝑇
0
𝑑𝑡 (10)
One of the most common way to describe vibration signals is using the RMS (Root Mean
Square) value, since it is in direct relation with the signal’s energy, it is determent by
following equation,
𝑥𝑅𝑀𝑆 = √1
𝑇∫ 𝑥(𝑡)2
𝑇
0
𝑑𝑡 (11)
Similar characteristics are used for vibration signals that are non-harmonic. When signal is
non-harmonic the practical use for peak- and peak-to-peak-values are hardly any. Since
usually the vibration signals measured in practice are non-harmonic, the RMS-value is
commonly used to describe the signal.
Often the ratio between the peak-value and the RMS-value is used to presume the
prevailing shape of the vibration signal waveform, this ratio is called Crest-Factor (CF) and
is determent with,
𝐶𝐹 =𝑥𝑃𝑒𝑎𝑘
𝑥𝑅𝑀𝑆 (12)
3.1.2 Vibration Signal Classification
Most machine component generate vibration when in operation, these vibrating signals
characterize these components and allows them to be separated from others. Distinguishing
faulty condition from healthy condition becomes possible when the characteristics are
known. The distinguishing features often results from different repetition frequencies, e.g.
gear-mesh frequencies, fault frequencies in rolling element bearings, and fluid flow
frequencies, such as turbulence or cavitation. The gear-mesh frequencies are usually at
harmonics of the rotational shaft speed, when fault frequencies of rolling element bearings
are generally not at harmonic with the shaft speed, and usually frequencies associated to
fluid flow are at random nature [1].
As mentioned, vibration signals are commonly distinguished by the repetition frequencies
of their periodic events, therefore one of the most common method of evaluating signals is
in terms of their frequency spectrum. The frequency spectrum shows how their constitutive
components are distributed by their frequency, and it is created with various forms of
Fourier analysis, which is described further in Chapter 4.
Vibration signals are categorized by their type, Figure 24 shows how the basic breakdown
looks like, with examples how the signals may look like in the time-domain and the
frequency-domain. The most fundamental division is separating into stationary and
nonstationary signals, where stationary signal means its statistical properties are invariant
52
with time, and nonstationary signal are all other signals that do not satisfy the stationary
condition [1].
Figure 24: Examples of different signal types and their spectral content.
Stationary Signals
Stationary signals are divided into two main categories, deterministic and random. Where
deterministic signals are composed entirely of discrete frequency sinusoids, therefore their
frequency spectrum consists of discrete lines at the corresponding frequencies of those
sinusoids. When the frequency, amplitude, and initial phase of these components are
known, their value is predictive at any time in the future or past, hence the term
‘deterministic’. Furthermore, deterministic signals are divided into two parts, periodic and
quasi-periodic. The frequency components of the periodic signal are at integer multiples or
harmonics of the fundamental periodic frequency, often the rotational speed of the
driveshaft. Vibration signal from a gearbox where the input frequency is constant is a
textbook example of a periodic signal. For the quasi-periodic signals, the frequency
components are not entirely all members of a harmonic series. Theoretically, this means
that the ratio between at least two frequency components must be irrational number,
otherwise the signal would be periodic. In practice it means that there is no direct link
between at least two of the frequency components. Commonly known example is a
vibration signal from a multiple driveshaft gas turbine, where each driveshaft generate
families of harmonics, but the total vibration signal is quasi-periodic [1].
Random signals are more complex since their value at any time cannot be predictive. But
for stationary random signals the statistical properties of the signal do not vary with time.
Only from those statistical properties, such as mean value, mean square value, and so on,
can the random signal properties be described [1].
Nonstationary Signals
Vibration signals that are generated when sudden changes occur in a system are called
nonstationary signals. This type of signal are divided into two categories, continuous and
transient. There is no concrete rule for separating these two, but usually it said that
transient signals only exist for a finite length of time and are commonly analyzed as an
entity. Typical example of a transient signal would be the impulsive force generated by a
hammer blow to a structure, and the impulse response of the structure.
53
Nonstationary vibration signal is said to be continuous if it consists of, sine components
with changing amplitudes and/or frequencies, or random signals with statistical properties
that change with time, or with transient appearing with varying intervals and with varying
characteristics in time and frequency [14]. Typical example of a nonstationary continuous
signal is the vibration generated from construction worker operating a jack hammer.
3.2 Vibration Signals from Rotating Machinery
Rotating machinery can include every possible type and variation of equipment and
components that operate on circular motion, it consists of a driver or prime mover, a drive
or power train, and a driven equipment. Electrical motors are a great example on prime
movers, other prime movers include internal combustion engines and turbines. General
definition on power train is the equipment or components that transfer the rotating energy
from the driver to the driven equipment, e.g. drive shafts, gears, belt drives, and couplings.
Driven equipment includes pumps, compressors, mixers, fans, blowers, generators, etc.
In the sense of condition monitoring, changes in vibration indicate change in operational
condition. It is important for maintenance personnel to know how common faults appear in
the vibration spectrum and be aware of any factors that cause these changes to occur and
try to eliminate them or at least reduce them significantly.
Vibration tends to change with operational speed and load, and to simplify, the focus will
be on constant speed and load machinery, where vibration signals from a fan setup, like the
one illustrated in Figure 25, will be discussed. Setups like this generally produce stationary
vibrating signals when in normal operation, occasionally the signals are nonstationary, but
those are usually generated when machine is undergoing run-up or run-down conditions
[1]. This type of setup is similar to the setup studied in the case study of this thesis.
Figure 25: Simplified schematic picture of centrifugal fan setup.
54
3.2.1 Shaft Frequency and its Harmonics
A number of faults occurring in rotating machinery manifest themselves at frequencies
corresponding to the rotating speed of the drive shaft and/or its low harmonics and
subharmonics. The most common faults that manifest themselves at the rotating shaft
frequency and possibly the first few harmonics are, unbalance, misalignment, bent shaft,
and cracked shaft. Faults manifesting itself at subharmonic shaft frequencies are more
common in machinery that includes journal bearings, usually caused by some type of
whirling or wiping, will not be discussed further in this study.
Unbalance
Unbalance occurs when the local center of mass (CoM) isn’t at the center of rotation,
resulting in exciting forces that generate increase in vibration. How the vibrating response
appear depends whether the unbalancing mass on the drive shaft is distributed axially or
localized at some point. Another factor contributing to the response is whether the shaft is
rotating below or over its first critical speed.
When the unbalancing mass is localized on the drive shaft the exciting force will be radial,
generating vibration response mainly in radial directions and very little axially, and the
phase difference between measurement points is 0°, this type of unbalance is commonly
known as static unbalance. If the unbalance is caused by more than one localized mass, the
resulting vibration response is generally in radial and axial directions and the phase
difference between measurement points is 180°, often referred to as dynamic unbalance.
Figure 26 illustrates a comparison between static and dynamic unbalance and how the
frequency response generally looks like, where the phase difference is between measuring
points one and two.
Figure 26: Comparison between static and dynamic unbalance.
Most often drive shafts are axisymmetric, and the bearing supports generally have different
stiffnesses in vertical and horizontal directions, resulting in a different vibration response
in the two directions. Bearings usually have nonlinear stiffness, meaning that even if the
unbalancing force is only acting at the rotating speed, the vibration response will be
distorted to some extend from sinusoidal and the spectrum will include some harmonics of
the shaft speed [1].
55
When the inertia of the rotor is distributed axially, the CoM can vary a lot between each
section, resulting in a radial unbalance force that changes in amplitude and direction
between sections. For a rigid rotor, these unbalance forces are combined into an equivalent
unbalance force that acts at the rotors global CoM and a moment about some CoM axis.
This simplification is done because the overall vibration response is a combination of
purely radial and rocking motions, where the rocking motions can generate axial responses.
Studying rotor motion becomes more complicated when the shaft is long and considered
flexible and operating above the first critical speed. This is often the case for large turbo
machinery like steam turbines in powerplants. This topic is covered in detail in the book
Turbomachinery Rotordynamics, by Dara Childs.
Misalignment
When two or more components are connected together, like electric motor to a drive shaft,
a coupling is used. Couplings are of various kind e.g. muff or sleeve, flanged, flexible,
gear, and etc. and what kind is used depends on machinery and desired functionality. When
components are connected together there’s possibility-for misalignment. There is
possibility for parallel misalignment or angular misalignment or combination of both, this
is illustrated in Figure 27. Often flexible couplings are used to mitigate the effects of
misalignment.
Figure 27: Comparison between possible misalignments in coupled connections.
Misaligned couplings contribute into the shafts bending deflections, where the shafts are
fixed spatially, but rotating with respect to the shafts. The induced bending moments thus
depend on the shafts bending stiffness and must be counteracted by forces at the bearings,
resulting in an increased vibration [1].
Parallel misalignment usually generates vibration response in the first two orders of the
shaft frequency, although sometimes vibration appears in the third order. Rarely vibration
appears on higher orders, but if the misalignment is great, vibration can appear on higher
orders possibly all the way to the eight. The direction of the vibration is in both axial and
radial direction and the vibration phase difference over the coupling is 180°.
Angular misalignment usually generates vibration response in the first two orders of the
shaft frequency, where the second order is dominant in axial direction. Often the third
order appears, but not always, and sometimes the fourth order appears. Like the parallel
misalignment, the angular generates vibration in both axial and radial direction with a
phase difference of 180° over the coupling. In Figure 28 a typical frequency spectrum for
both parallel and angular misalignments are illustrated.
56
Figure 28: Typical frequency spectrums for parallel and/or angular misalignments.
Mechanical Looseness
Mechanical looseness can occur at three locations, internal assembly, looseness at machine
to baseplate interface, and structure looseness. Figure 29 illustrates these three types of
mechanical looseness and their typical vibration response. Structure looseness, type A,
occurs when the machines structure is loose or there are weaknesses in the machine’s feet,
baseplate, or base. This results with an increase in vibration response in the first order of
the shaft frequency, with 180° phase difference between base and baseplate. Mechanical
looseness, type B, is associated with loose bolts, cracks in the frame structure or the
bearing pedestal. This results in an increase in vibration in the second order and often
shows response also in the half and third orders, sometimes even higher orders. Internal
assembly looseness, type C, is e.g. between a bearing liner in its cap, a bearing on a shaft,
or an impeller on a shaft. This kind of looseness is often caused by an improper fit between
component parts. This often results in vibration in the bearings natural frequency region
and multiple orders of the shaft frequency. Noting that looseness of this kind often
generates sub-harmonic multiples at exactly half order and one third order [3].
Figure 29: Examples of mechanical looseness and typical vibration response.
57
Bent Shaft
If a drive shaft gets damaged that results in a permanent bow, the results will be excessive
vibration response that is a combination of misalignment and unbalance responses [1].
Shaft can acquire permanent bow from numerus events e.g. excessive loading, faulty
installation, or rubbing.
Cracked Shaft
Development of a crack in a drive shaft is one of the most serious faults to be detected in
condition monitoring, especially in large machinery, like steam turbines. Where a crack
that is permanently open increases vibration primarily in the first two orders of the shaft
frequency. Crack that opens and closes in each revolution, often referred to as a breathing
crack, also give a rise in vibration at the third order [1]. This topic is covered in detail in
the book Cracked Rotors: a survey on static and dynamic behavior including modelling
and diagnosis, by N.Bachschmid, P. Pennacchi, and E. Tanzi.
3.2.2 Rolling Element Bearings
There are two main types of bearings used in modern industry, rolling contact, and sliding
contact. The main difference is that in rolling contact bearings the main load is transferred
through rolling elements, sliding contact bearing does not have any rolling elements and
operates in a way that the shaft slides in a lubricated sleeve or bushing. For the remainder
of this thesis only rolling contact bearings will be discussed, referring to chapter 12 in [15]
and section 2.2.1.4 in [1] for more information about sliding contact bearings.
Rolling contact bearings, also known as rolling element bearings (REB), are one of the
most widely used components in machines. They are manufactured to take pure radial
load, pure axial load, or combination of both radial and axial loads. Common types of
roller and ball bearings, and nomenclature of a ball bearing are illustrated in Figure 30,
showing also the four fundamental parts of REB, the inner and outer raceways, rolling
elements, and the separator (usually referred to as the cage).
Figure 30: Nomenclature of a ball bearing and common types of roller and ball bearings.
58
Failure of REB is one of the most common reasons for machine breakdown, hence, the
vibration signals generated by bearing faults have been widely studied. There are powerful
diagnostic techniques available to analyze these signals, see section 4.2 for further
discussions on that topic. These faulty bearing vibration signals, often referred to as
bearing frequencies, are defined from the geometry of the bearing and the shaft speed. The
four fundamental components, as described earlier, all generate a distinguishing vibration
when they wear out or get damaged. The formulae for the defect bearing frequencies,
applies to all types of REB, are as follows:
𝑂𝑢𝑡𝑒𝑟 𝑅𝑎𝑐𝑒 𝐹𝑎𝑢𝑙𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦: 𝐵𝑃𝐹𝑂 =
𝑛 𝑓𝑟
2(1 −
𝑑
𝐷cos 𝛼) (13)
𝐼𝑛𝑛𝑒𝑟 𝑅𝑎𝑐𝑒 𝐹𝑎𝑢𝑙𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦: 𝐵𝑃𝐹𝐼 =
𝑛 𝑓𝑟
2(1 +
𝑑
𝐷cos 𝛼) (14)
𝑅𝑜𝑙𝑙𝑖𝑛𝑔 𝐸𝑙𝑒𝑚𝑒𝑛𝑡 𝐹𝑎𝑢𝑙𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦: 𝐵𝑆𝐹 =
𝐷 𝑓𝑟
2 𝑑[1 − (
𝑑
𝐷cos 𝛼)
2
] (15)
𝐶𝑎𝑔𝑒 𝐹𝑎𝑢𝑙𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦: 𝐹𝑇𝐹 =
𝑓𝑟
2(1 −
𝑑
𝐷cos 𝛼) (16)
Where fr is the shaft frequency, n is the number of rolling elements, d is the diameter of the
rolling elements, D is the pitch diameter, and α is the contact angle. Noting that the BSF is
the frequency with which the fault strikes the same raceway, inner or outer, so usually
there are two impacts per period, hence the even harmonics of BSF are often dominant [1].
Under normal conditions, rolling element bearings operate most of their lifetime defect
free, often around 80%. REB faults are usually divided into four stages, where these four
stages represent approximately the last 20% of the bearing lifetime. For easier distinction
between these stages, the vibration frequency spectrum is divided into four zones of
interest. Figure 31 illustrates bearing life model and possible vibration spectrum of these
four stages, noting that the frequency and amplitude axis are not linearly drawn and the
spectral content is simply a snapshot at some time and isn’t meant to imply constant
features throughout each stage [16]. In zone 1 they are always some frequency components
that correspond to the rotation of the shaft and its harmonics, since perfectly balanced
system is not achievable in practice.
Stage 1
First stage of bearing failure appears with low amplitude vibration in the ultrasonic
frequency range, approximately 20 to 60 kHz. Usually it begins with subsurface cracking,
often 0.1 to 0.125 mm below the surface of the inner or outer raceways and are normally
not revealed during a visual examination. Few techniques have been developed by vendors
like SKF, IRD, CSI, and SPM to evaluate these frequencies e.g. Spectra Emission Energy
(SEE), Spike Energy Spectrum (gSE), High Frequency Detection (HFD), and Shock Pulse
Method (SPM) [16].
59
Stage 2
Second stage of bearing failure is when the fault progresses into microscopic pits on the
failed component surface. These defects are invisible to the naked eye and require
magnification to see, and as the failure develops, the pits evolve into cracks, spalls, flakes,
etc. The impacts generated by the microscopic pits cause the bearing components to
resonance at their natural frequencies, often referred to as bearing ringing. The natural
frequencies are usually in the frequency range between 2 kHz and 20 kHz, depending on
the design and mechanical parameters. As the failure progresses the impacts get greater
and periodicity is seen in the vibration spectrum, and peaks with sidebands start to appear
at the natural frequencies, and the ultrasonic frequency peaks grow. At the end of stage 2,
bearing defect frequencies start to appear, sideband frequencies may also be present below
and above the defect frequencies [16].
Figure 31: Bearing life model, showing common frequency spectrums for each stage.
Stage 3
Further progression of the failure causes the initial cracking, spalling, and/or flaking that is
visually apparent in the raceways and/or on the rolling elements but is still confined to the
bearing itself. The vibration signal generated from the impacts is strong enough to generate
peaks at the bearing defect frequencies. The amplitude of the defect frequencies increases,
60
and harmonics appear as the failure progresses. Eventually, other defect frequencies are
detected in both the bearing defect and natural frequency zones of the vibration spectrum.
In addition to the increasing harmonics of the defect frequencies, there are sidebands
associated with the shaft frequency, and the ultrasonic frequency peaks trend upward. The
damage on the components can now easily be seen through visual inspection of the
bearing. The defects frequencies will show different characteristics, depending on where
the fault originates, summarizes as follows: [16]
• BPFO: For outer raceway fault, there are harmonics at the BPFO, usually lower in
amplitude than the main or fundamental harmonic of the BPFO. As the fault
progresses, the amplitude of the harmonics will increase to be higher than the
fundamental harmonic of the BPFO.
• BPFI: For inner raceway fault, there are harmonics of the BPFI along with
sidebands of the shaft frequency. These sidebands are seen at the fundamental
harmonic of the BPFI and its harmonics. As the shaft rotates, the inner raceway
fault vibration peak will rise and fall as the raceway moves through the loading
zone of the bearing.
• BSF: For rolling element fault, a vibration peak may be noticed at the BSF. Since
the fault on the rolling element will strike the outer and inner raceway once per
revolution of the shaft, a vibration peak may be noticed at two times the BSF. Also,
there will be some sidebands around the harmonics of the BSF, these sidebands are
generated at the FTF.
Stage 4
Fourth and final stage of bearing failure is when multiple faults have progressed, and the
bearing is at its end of the life. Often the rolling elements have begun to deform, and the
cage starts to disintegrate or break, at this stage the defect frequencies often start to
disappear. The shaft has more clearance to move around inside the bearing, because of
degradation, resulting in increased amplitude of the shaft frequency and its harmonics. The
noise floor of the entire vibration spectrum often increases because the generated
frequencies will not necessarily occur at the same time interval as before. Defects are no
longer localized, but are distributed around the raceways, resulting in numerous modulated
frequencies and harmonics. The defect frequencies in both zones 2 and 3 are replaced with
a random broadband high frequency component. The ultrasonic frequency peaks may
decrease, but usually it grows excessively just before total failure. Stage 4 bearing failure
usually progresses as follows: [16]
• Impact points are worn out, leaving no sharp edges, reducing defect frequencies in
the high frequency resonance region.
• Periodicity of the impact response decreases, and the amplitude of the defect
frequencies start to decrease.
• Clearance increases as degradation increases, resulting in increased vibration of the
shaft frequency and its harmonics.
• The R.M.S. value of the time domain data start to increase.
Usually, bearing degradation is linear for some period of time and can possibly be trended,
but often as the bearing approaches its end of lifetime the process becomes nonlinear.
61
3.2.3 Bladed Machines
Machines that have a number of uniformly spaced blades or vanes on the rotor are often
referred to as bladed machines e.g. compressors, turbines, fans, and pumps. These blades
or vanes interact with components on the stator to give a periodic excitation of the casing.
The most basic frequency is called blade pass frequency (BPF), and is calculated as,
𝐵𝑙𝑎𝑑𝑒 𝑃𝑎𝑠𝑠 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦: 𝐵𝑃𝐹 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑙𝑎𝑑𝑒𝑠 ∗ 𝑠ℎ𝑎𝑓𝑡 𝑓𝑟𝑒𝑞𝑢𝑎𝑛𝑐𝑦 (17)
If a machine is in good condition, these interactions are generally rather small, meaning
that the flow of medium from the rotor is guided to have the correct angle to correspond to
the angle of any guide blades or vanes on the stator. If the medium flow angles change,
more impulsive interactions will occur, resulting in a change in the vibration spectrum.
Medium flow angles can change for various reasons e.g. a blade gets damaged, fouling
buildup, blade wear, or guide blades change for some reason [1]. Figure 32 illustrates how
three typical frequency spectrums, for bladed machines, may look like, including a
diagram of a centrifugal fan.
The frequency spectrum numbered with the letter A, corresponds to the circumstances
described earlier, where the flow angles change. Another example, marked with the letter
B, shows how random sub-harmonic vibration response is generated when the medium
flow is of turbulence nature The third case, marked with letter C, shows how random high
frequency vibration response is generated when cavitation is present, noting that usually
cavitation is recognizable by human hearing.
Figure 32: Frequency spectrums of common cases on bladed machines.
63
4 Signal Processing and Analysis
In condition monitoring the ability to extract relevant information regarding the condition
of machinery is crucial. The science of extracting this information from measured vibration
data signals is called signal processing. Normally this means determining from
measurements certain characteristics that help identifying the source of the vibration to be
able to take appropriate actions to reduce or control it. In this chapter common signal
processing techniques will be introduced and discussed, also, few methods used in fault
detection and diagnostics will be introduced, especially regarding a machine setup
introduced in Figure 25.
4.1 Signal Processing Techniques
Nowadays virtually every signal processing procedure is accomplished using digital
instrumentation. Most vibration transducers used in practice produce analog voltage
signals, that contains some sort of noise. Hence, the first step in signal processing is
usually signal conditioning, followed up with suitable analysis method e.g. Fourier
Analysis.
4.1.1 Signal Conditioning
The most basic definition of signal conditioning is it is a set of operations applied to raw
measurement data, to improve its quality before its processed further. There are multiple
methods that fall under the hat of signal conditioning e.g. amplification, filtering, analog to
digital conversion. Commonly, vibration measurements are conducted using acceleration
sensor connected to signal analyzer. Commercially produced signal analyzers often include
many features for signal conditioning, like analog and digital filtering, excitation,
amplification, analog to digital conversion, and some have configurations to analyze the
signal with Fourier analysis or other known methods.
Amplification
In general, the signals generated by vibration transducers are very weak and have high
impedance. Hence, they cannot be transmitted to the analyzing equipment. These signals
must be amplified before they are processed further. There is a vast variety of different
types vibration transducers manufactured that have different applications and functions.
Vibration transducers are often manufactured with internal electric circuits that include
amplification. As described in section 2.3.1, the piezoelectric acceleration sensors are often
manufactured with a built-in electric circuit, often referred to as IEPE-acceleration sensors
(Integral Electronics Piezoelectric sensors). This internal circuit converts the high
impedance electric charge, produced by the piezoelectric material, into low impedance
voltage signal, which is possible to transmit over long distances.
64
Figure 33 illustrates an operational amplifier, where the amplification is controlled by the
size of the resistors Rin and Rf. Referring to [17], for more detailed discussion about
operational amplifiers and their applications.
Figure 33: Basic non-inverting operational amplifier circuit.
Analog to Digital Conversion
Analog to digital converter (ADC) is a device that is used to convert a continuous analog
signal, which represent an uncountable dataset, into countable dataset, referred to as digital
signal. There are various designs of ADC available on the market, manufactured for
various tasks. For high sampling frequency requirements, the most common design is the
Delta-Sigma type converter, a schematic diagram of this type is illustrated in Figure 34.
Delta-Sigma converters have the following three key features: [14]
• The analog input signal goes through an integrator to a clock driven comparator,
that acts as a one-bit ADC.
• The feedback from the one-bit comparator output goes through one-bit digital-to-
analog converter, which is subtracted from the analog input signal.
• Averaging operation with low-pass digital filter, resulting in increased number of
bits that form the digital value of the output.
Figure 34: Schematic diagram of a Delta-Sigma type analog-to-digital converter [14].
65
The final digital data frequency range for an ADC varies with one of the following two
procedures, [14]
• Lock the digital filter cutoff frequency, so it corresponds to the sampling rate of the
comparator.
• Fix the sampling rate of the comparator, then control the frequency limit and rate of
the digital output data by varying the cutoff frequency of the digital filter and the
decimation degree.
In both cases, the process of oversampling that is followed by low-pass digital filtering and
decimation, increase the effective resolution of the digital output signal by suppressing the
spectral density of the digital noise in the output [14].
Referring to chapter 27 in [11], for more detail discussion about the design and function for
ADC´s.
Resolution and Sampling Rate
There are many specifications that describe ADC performance capability, but there are two
main features that usually are used when comparing ADC capability. One being the
number of bits the ADC uses to form the digital signal and therefore decides its resolution,
and the other one being the sampling rate, that sets the frequency limit of the measured
analog signal.
When converting continuous analog signal into digital sequence with finite number of
possible values imposes resolution problem in the form of a round-off error. ADC
resolution is a function of the number of bits (β) used to form the digital output value and it
is defined in several ways, like Span, Dynamic Range, Peak Signal-to-Noise Ratio, and
Signal-to-Noise Ratio [14].
Span (S): Span is defined as the total number of possible digital values provided by the
output of ADC. Excluding zero, the span is given by the following equation,
𝑆 = 2𝛽 − 1 (18)
Dynamic Range (DR): The dynamic range of an ADC is defined as the ratio of the largest
output value (either positive or negative) to the smallest output value. Excluding the sign
bit if used (one bit to define sign polarity) and assuming a mean value of zero, the dynamic
range in decibels (dB) is given by,
𝐷𝑅 = 10 ∗ log10(2𝛽 − 1)2
≈ 6 ∗ 𝛽 𝑓𝑜𝑟 𝛽 > 5 (19)
Peak Signal-to-Noise Ratio (PS/N): The ratio of the largest value (positive or negative) to
the standard deviation of the digital noise in the output signal, is the definition of the peak
signal-to-noise ratio. With Δx representing the magnitude interval of the output signal, the
standard deviation of digital noise is defined in [18], with the following equation,
𝜎𝑛 =
∆𝑥
√12 (20)
66
Like before, excluding the sign bit if used and assuming a mean value of zero, the peak
signal-to-noise ratio in decibels (dB) is given by,
𝑃𝑆
𝑁= 10 ∗ log10 [12(2𝛽 − 1)
2] ≈ 6 ∗ 𝛽 + 11 𝑓𝑜𝑟 𝛽 > 5 (21)
Signal-to-Noise Ratio (S/N): The ratio of the maximum standard deviation of the signal,
without clipping to the standard deviation of the digital noise in the output signal, is the
definition of the signal-to-noise ratio. S/N in decibels (dB) is given by the following
equation,
𝑆
𝑁=
𝑃𝑆
𝑁− 10 ∗ log10 (
𝑃
𝜎𝑠)
2
(22)
Where σs represents standard deviation of the signal, P represents the peak value of the
signal, and PS/N is defined earlier as peak signal-to-noise ratio. Noting that if the signal is
a sine wave, the ratio (P/σs)2 is equal to 2 (approx. 3 dB), and for random signals it is equal
to 9 (approx. 10 dB) [14].
Table 2 summarizes the difference in span, dynamic range, and signal-to-noise ratio for
common bit size ADC. Where the values displayed assume the mean value of the signal is
zero, which is usually the case for noise and vibration signals. Noting, the values are the
theoretical maximum values, where in practice there are various factors that can reduce the
effective bit size by one or more bits [18].
Table 2: Span, Dynamic Range, and Signal-to-Noise comparison between common bit size
Analog-to-Digital Converters.
Choosing appropriate sampling rate for the analog to digital conversion is governed by the
Nyquist sampling theorem, it establishes a sufficient condition for a sample rate that
ensures that all the information from the analog signal are captured. The Nyquist-
frequency, fN, defines the upper frequency limit for the digital data, it is given by the
following equation, where SR is the sampling rate in samples per second [14].
𝑓𝑁 =
𝑆𝑅
2 (23)
If the analog signal has any information at a frequency above the Nyquist frequency, it will
be interpreted as information that are at a frequency lower than the Nyquist frequency.
This phenomenon, where information above the Nyquist frequency are folded back to
Number of Bits
(excl. sign bit)Span DR (dB) PS/N (dB)
S/N (dB)
Sine Wave
S/N (dB)
Random
8 255 48 59 56 49
10 1.023 60 71 68 61
12 4.095 72 83 80 73
16 65.535 96 107 104 97
24 16.777.215 144 155 152 145
67
frequencies below the Nyquist frequency, is called aliasing. Figure 35 illustrates how
aliasing works, where an original signal that is at 5 Hz is measured at a sampling rate of 6
samples per second, generates aliasing signal at 1 Hz. According to Nyquist sampling
theorem, the sampling rate should be at least two times higher than the frequency of the
signal of interest.
Aliasing is considered a serious error because once the analog-to-digital conversion is
complete, it is hard to recognize if aliasing occurred, and even though its known that
aliasing occurred, its generally impossible to correct the data for the resulting error [14].
Figure 35: Frequency aliasing due to an inadequate sampling rate.
Signal Filtering
The most common types of filters used in vibration signal processing include, low-pass,
high-pass, band-pass, and band-reject filters. Nowadays filtering is generally performed
digitally after the analog to digital conversion, to isolate a selected frequency range of
interest.
Low-pass filter is the most frequently used filtering method in vibration signal processing,
as the name implies it removes the signal content above the designed cutoff frequency.
Low-pass filtering is performed both analytically and digitally. To prevent aliasing in the
digitalized signal its crucial to filter the initial analog signal from the vibration transducers
with an analog low-pass filter, with cutoff frequency below the Nyquist frequency.
Theoretically the cutoff frequency could be half the Nyquist frequency, but no analog filter
has a perfect frequency cutoff, hence the sampling rate is often selected to be at least 2.5
times the cutoff frequency [19].
High-pass filter, like the name implies is the converse of the low-pass filter, it removes
signal content below the designed cutoff frequency and thus passes through the remaining
part of the signal. Frequencies above ten times the rotating speed of the drive shaft, are
generally not focused on in condition monitoring of rotating machinery, hence the use of
high-pass filtering is not commonly used in this field [19].
Band-pass filter is designed to remove signal content that is outside the designed
frequency range of the filter. Band-pass filter is basically a low-pass filter and a high-pass
filter connected in series, where the cutoff frequency of the low-pass filter is higher than
the cutoff frequency of the high-pass filter.
68
Band-reject filter is designed to be like an opposite of the band-pass filter, it removes all
signal content within specific frequency bandwidth.
To describe how filter behaves a response curve is used. The response curve is basically a
graph showing the frequency versus VOUT / VIN ratio, known as the attenuation ratio.
Usually the attenuation ratio is expressed in units of decibels (dB) and the frequency in
Hertz (Hz). Filter response curves are most commonly plotted with decibels on the y-axis
and logarithmic frequency on the x-axis. The cutoff frequency is generally defined where
the power of output signal has reduced by three decibels, -3dB [20].
Figure 36 illustrates how the response curves generally look like for the four main filter
types. The term, center frequency (f0) is used for band-pass and band-reject filters, and it is
the central frequency that lies between the upper (f2) and lower (f1) cutoff frequencies.
Generally, the center frequency is defined as the arithmetic mean of the upper and lower
cutoff frequencies. The width of the filter passband is generally called bandwidth (B.W.),
where the passband is the band of frequencies that do not experience significant
attenuation, often the -3-dB mark, when passing through the filter. Filters stopband
frequency (fs) is where the attenuation reaches certain value, for low-pass and high-pass
filters the stopband is defined as the frequencies beyond the stopband frequency, and for
bandpass and band-reject filters there are two stopband frequencies, where the frequencies
between these two are referred to as the stopband [20].
Figure 36: Coparison between response curves for the four main filter types.
In chapter 5.2 the process of designing analog low-pass filter with cutoff frequency of
1000 Hz will be introduced. Referring to [21] for more detailed discussion about analog
and digital filtering.
69
Windowing
Vibration signals generated from a rotating machinery usually do not have the period
significantly marked and the course of the signal in different periods is not exactly same.
Since, Fourier transformation assumes a periodic function, the resulting frequency
spectrum is distorted, usually referred to as leakage, unless appropriately handled. To
prevent a leakage error, the vibration signal and a weighting function are multiplied in the
time domain. This procedure is generally known as applying windowing to the vibration
signal. There are numerus different types of weighting windows used in practice which
have different characteristics, but the most commonly used in vibration measurements are
rectangular (no window), Hanning, and Flat Top [12].
Figure 37 illustrates an example on how frequency spectrums may look like for two
identical signals, except that one’s sample period results in a periodic signal but the other
one results in a non-periodic signal, resulting in a leakage error for the non-periodic signal.
Figure 37: Leakage error appearing in frequency spectrum, for the non-periodic signal.
According to [22] the following equations define the weighting functions for Rectangular,
Hanning, and Flat Top weighting windows
Rectangular: 𝑤(𝑡) = 1 , 0 ≤ 𝑡 < 𝑇
𝑤(𝑡) = 0 𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒 (24)
Hanning: 𝑤(𝑡) = 1 − cos (2𝜋
𝑡
𝑇) , 0 ≤ 𝑡 < 𝑇
𝑤(𝑡) = 0 𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
(25)
Flat Top: 𝑤(𝑡) = 1 − 1.93 cos (2𝜋
𝑡
𝑇) + 1.29 cos (4𝜋
𝑡
𝑇) − 0.388 cos (6𝜋
𝑡
𝑇)
+ 0.0322 cos (8𝜋𝑡
𝑇) , 0 ≤ 𝑡 < 𝑇
𝑤(𝑡) = 0 𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
(26)
70
In Figure 38 the difference between these weighting functions is illustrated. When
analyzing continuous vibration signals the Hanning weighting is generally used for most
cases and should be used with 66.6% or 75% overlap when analyzing true real-time data.
The Flat Top weighting function is namely used for calibration or when correct amplitude
measurement is needed, e.g. for balancing measurements. Rectangular weighting should be
avoided for most cases, unless for special cases e.g. transient signal analysis [22].
Figure 38: Comparison between Rectangular, Hanning, and Flat Top weighting functions.
4.1.2 Fourier Analysis
The following section supports highly on chapter 3 in Robert Bond Randall’s book
Vibration-based Condition Monitoring [1].
Fourier Series
The basic function of Fourier analysis is to break down signals and represent them as
summation of sinusoidal components. Almost all signals decompose in this manner, with
very few exceptions. This method is commonly used in practice for machine vibration
analysis on periodic signals, generally produced by machines that run at a constant speed
[1].
Any periodic signal is possible to represent with the following equation, also known as a
Fourier series,
𝑔(𝑡) =
𝑎0
2+ ∑ 𝑎𝑘 cos(𝑘𝜔0𝑡)
∞
𝑘=1
+ ∑ 𝑏𝑘 sin(𝑘𝜔0𝑡)
∞
𝑘=1
(27)
where the fundamental angular frequency is represented with ω0. The coefficients are
obtained as follows,
71
𝑎𝑘 =2
𝑇∫ 𝑔(𝑡) cos(𝑘𝜔0𝑡) 𝑑𝑡
𝑇/2
−𝑇/2
(28)
𝑏𝑘 =2
𝑇∫ 𝑔(𝑡) sin(𝑘𝜔0𝑡)
𝑇/2
−𝑇/2
𝑑𝑡 (29)
where one period of the signal is represented with T.
The division into sine and cosine components, for a given periodic signal, depends on an
arbitrary assignment of zero time [1]. The total component at frequency ωk (=k ω0) is
therefore given by
𝐶𝑘 cos(𝜔𝑘𝑡 + 𝜑𝑘)
𝑤ℎ𝑒𝑟𝑒, 𝐶𝑘 = √𝑎𝑘2 + 𝑏𝑘
2 𝑎𝑛𝑑 𝜑𝑘 = tan−1 (𝑏𝑘
𝑎𝑘)
(30)
Clarifying that the sinusoid has a constant amplitude, with the phase angle being that
existing at the arbitrarily defined zero time, where a different time zero only affect the
initial phase φk [1].
By interpreting expression (30) as a sum of rotating vectors, each of length Ck /2, where
one rotates at angular frequency ωk and the other at -ωk, with initial phase φk and -φk , it is
represented as,
𝐶𝑘
2𝑒𝑗(𝜔𝑘𝑡+𝜑𝑘) + 𝑒−𝑗(𝜔𝑘𝑡+𝜑𝑘) (31)
With this interpretation of the Fourier series, an alternative version of equation (27) can be
presented as,
𝑔(𝑡) = ∑ 𝐴𝑘𝑒𝑗𝜔𝑘𝑡
∞
𝑘=−∞
(32)
now the coefficients Ak are complex, and the phase shift is incorporated in the given form,
𝐴𝑘 =
𝐶𝑘
2𝑒𝜑𝑘𝑡 (33)
which is altered as,
𝐴𝑘 =1
𝑇∫ 𝑔(𝑡)𝑒−𝑗𝜔𝑘𝑡
𝑇/2
−𝑇/2
𝑑𝑡 (34)
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Fourier Transform
Non-periodic signals can also be expressed as a sum of complex exponentials, known as
the Fourier transform. The Fourier transform is obtained from the Fourier series by letting
the periodic time to tend to infinity and removing the division by T [1]. Equations (32) and
(34) then become
𝐺(𝑓) = ∫ 𝑔(𝑡)𝑒−𝑗2𝜋𝑓𝑡𝑑𝑡
∞
−∞
(35)
𝑔(𝑡) = ∫ 𝐺(𝑓)𝑒𝑗2𝜋𝑓𝑡𝑑𝑓
∞
−∞
(36)
where the continuous frequency function f, expressed in Hz, replaces the angular frequency
ωk (rad/s). Equations (35) and (36) are commonly known as the forward and inverse
Fourier transforms and are almost symmetrical, the only difference is being the sign of the
exponent [1].
Sampled Time Signals
Every type of signal that is to be processed digitally must be digitized or discretely
sampled. This results in the inverse case of the Fourier series, where the spectrum is
sampled discretely. The symmetry of the Fourier transform means that the sampled time
signal spectrum is periodic [1]. The forward and inverse transforms for the corresponding
version are,
𝐺(𝑓) = ∑ 𝑔(𝑡𝑛)𝑒−𝑗 2𝜋 𝑓 𝑡𝑛
∞
𝑛=−∞
(37)
𝑔(𝑡𝑛) =1
𝑓𝑠
∫ 𝐺(𝑓)𝑒𝑗 2𝜋 𝑓 𝑡𝑛 𝑑𝑓
𝑓𝑠2
−𝑓𝑠2
𝑤ℎ𝑒𝑟𝑒 𝑡𝑛 = 𝑛∆𝑡 =𝑛
𝑓𝑠
(38)
Discrete Fourier Transform
In principle all sampled time signals are of infinite length, however the record length is
always finite. This leads to the same situation as with the Fourier series, where the
spectrum is discrete, and the time record implicitly periodic. Resulting in a combination of
Fourier series and sampled time signal so that both the time record and frequency spectrum
are periodic and discretely sampled. The continuous infinite integrals of the Fourier
transform therefore become finite sums [1]. Commonly expressed as,
73
𝐺(𝑘) =1
𝑁∑ 𝑔(𝑛)𝑒−𝑗 2𝜋 𝑘
𝑛𝑁
𝑁−1
𝑛=0
(39)
𝑔(𝑛) = ∑ 𝐺(𝑘)𝑒𝑗 2𝜋 𝑘 𝑛𝑁
𝑁−1
𝑘=0
(40)
This is known as the discrete Fourier transform (DFT), and it corresponds closely to the
Fourier series in that the forward transform is divided by the record length N, which gives
correctly scaled Fourier series components. The scaling must be adjusted accordingly if the
DFT is to be used with other signal types e.g. stationary random or transient signals [1].
DFT’s forward operation is interpreted as the matrix multiplication,
𝑮𝑘 =
1
𝑁𝑾𝑘𝑛𝒈𝑛 (41)
where Gk represents the vector of N frequency components, the G(k) of equations (39) and
(40), gn represents the N time samples g(n), and Wkn represents a square matrix of unit
vectors exp(-j2πkn/N) with angular orientation depending on the time sample index n (the
columns) and frequency index k (the rows), this is illustrated graphically in Figure 39 (note
the rotated real and imaginary axes) [1].
Figure 39: Matrix representation of the DFT.
The zero-frequency value G(0) is simply the mean value of the time samples g(n), for the
case where k = 0, as would be expected. When k = 1 the unit vector rotates -1/N-th of a
revolution for each increment of the time sample, giving one complete revolution after N
samples (note that the revolution is negative). When the value of k gets higher, the rotation
speed gets proportionally higher. For k = Nyquist frequency (half the sampling frequency)
the vector turns through -π for each time sample, but it is impossible to see which direction
it rotated. For k higher than the Nyquist frequency the vector rotates (in negative direction)
more than π but is more easily interpreted as having turned through less than π (in the
positive direction). When the time signal is filtered with low-pass filter at half the sampling
frequency (which should always be the case) the second half of Gk will include the
negative frequency components that range from minus one-half the Nyquist frequency to
just below zero [1].
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Fast Fourier Transform
Calculating the DFT in practice is very time consuming and for some cases impossible
because of the size of computer memory required to complete calculations. To reduce and
simplify these calculations the fast Fourier transform (FFT) algorithm was developed,
which is a very efficient method to calculate the DFT. Looking at the matrix version of the
DFT (equation (41)) it has a form called the radix 2 algorithm where the FFT is based on N
being a power of 2 and factorizes an altered version of the Wkn matrix into log2N matrices
each holding the property that it only requires N complex operations to multiply with them,
instead of N2 operations if multiplied directly. Resulting in reduction in total complex
operations from N2 to N log2 N, e.g. for the common case where N = 210 = 1024 the
reduction is by over the factor of 100 [1].
In Figure 40 the matrix B, a modified version of the matrix Wkn, is illustrated, where the
rows are shifted in a bit reversed order from Wkn. This results in the most significant bit is
indexed rather than the least significant bit, and with increasing row number the phase
increments go from coarse to fine. When multiplying with B the results are in bit reversed
order as well but rearranging to correct address is an easy operation and takes short time
compared to the multiplication [1].
Figure 40: Matrix B, a modified version of matrix Wkn, with rows shifted.
For N = 8 and therefore log2N = 3, the matrix B is factorized into three matrices e.g. X, Y,
and Z where each row contains only two none-zero elements, one being unity. This
factorization is illustrated in Figure 41, where multiplication by each factor matrix only
requires N complex multiplication and additions. This arrangement of the factor matrices
illustrates how the decomposition can easily be extended to higher powers of 2, where the
factor matrices contain increasingly finer rotation, but the top left sub-matrix is always in
the form,
[ 𝐈 𝐈𝐈 −𝐈
]
In special cases further time saving operations are made e.g. radix 4 and radix 8
transforms, and also by factorizing with other than the power of 2, along as the properties
are those of the DFT [1].
75
Figure 41: Factorization of matrix B into three factor matrices X, Y, and Z.
Zoom FFT
When performing DFT the frequency ranges from zero to half the sampling frequency
(Nyquist frequency) with resolution equal to the sampling frequency fs divided by the
number of samples N. In some cases, it is necessary to analyze some parts of the frequency
spectrum in more detail with method called zoom-analysis. As mentioned before the
resolution is defined as,
∆𝑓 =𝑓𝑠
𝑁
and the two ways to improve it are either to increase the number of samples or reduce the
sampling frequency. In modern computers there are virtually no limits on the transform
size performed, so zoom analysis can easily be performed by an oversized transform and
then simply view small part of the solution. Reducing the sampling frequency is done with
shifting the center of the frequency band of interest to zero frequency, so that the frequency
band of interest can easily be isolated with a low-pass filter. The sampling frequency can
then be reduced accordingly, without aliasing errors, since the highest frequency is the half
of the zoom band [1].
4.1.3 Envelope Analysis
A frequency spectrum of a raw vibration signal often contains little information about
bearing faults regarding diagnostic purposes, and over the years Envelope analysis has
been established as one of the main diagnostics methods for bearing faults. When a rolling
element strikes e.g. local fault on the inner or outer raceways of the bearing it generates an
energy impulse that excites the structural resonance frequencies of the bearing and
increases the overall measured RMS value. The Envelope analysis is based on amplitude
modulation on the resonance frequencies at the characteristic defect frequency, and then
demodulate one of the resonances. The condition of the bearing can then be found with
analyzing the envelope frequency spectrum [23].
To carry out an Envelope analysis, the three following steps are involved:
• Step 1: The vibration signal is filtered with band pass filter around the resonance
frequencies, where the energy impulses are amplified.
• Step 2: The band-passed signal is then amplitude demodulated with Hilbert
transformation.
• Step 3: The envelope signal is then extracted and transformed with FFT to obtain
the envelope spectrum for further analysis and diagnosis on the bearing condition.
76
In practice one wishes to band pass filter just around the amplified resonance frequencies
and then obtain the envelope signal with Hilbert transformation e.g. with taking the
analytic signals amplitude spectrum. The complex analytic signal xa(t), is composed of the
real part of the raw signal x(t) and the Hilbert transform of x(t) which is the imaginary part.
The analytic signal is defined as,
𝑥𝑎(𝑡) = 𝑥(𝑡) + 𝑖𝐻(𝑥(𝑡)) (42)
where the Hilbert transform is defined as,
𝐻(𝑥(𝑡)) =1
𝜋∫
𝑥(𝑡)
𝑡 − 𝜏
∞
−∞
𝑑𝜏 (43)
analytic signals envelope can then by calculated with,
𝑎(𝑡) = √𝑥(𝑡)2 + 𝐻(𝑥(𝑡))2 (44)
the envelope spectrum can then be obtained by taking the FFT of the envelope signal,
making it easier to gather information about the impact frequencies and the condition of
the bearing [23]. In Figure 42 the Envelope analysis procedure using the Hilbert transform
method is illustrated.
Figure 42: Envelope analysis procedure using the Hilbert transform method.
4.2 Detection, Diagnostics, and Prognostics
When a machines operating condition starts to indicate that something is not normal, the
first step in the following process, is detecting the faulty state of the machine, followed by
diagnosing the cause of the fault, and finally establishing a reliable prognosis. The
detection of faults is very different when comparing rotating machines, reciprocating
machines, and internal combustion engines. As mentioned in section 3.2, the machine
setup studied in the case study of this thesis is illustrate in Figure 25, so for the remainder
of this chapter only detection, diagnostics, and prognosis regarding rotating machinery will
be discussed, where the main focus will be on REB.
77
4.2.1 Fault Detection
When determining whether some significant changes has occurred in the operating state of
machinery, the measured vibration signal must be processed. The technicians that carry out
the vibration measurements must usually be much more efficient than the one’s working
on diagnostics and prognosis, since commonly there are significant changes in just around
2% of cases analyzed [1].
There are many ways to detect machines fault, one way is to compare the machines
vibration level with standard vibration severity criteria. There are numerus standard
criteria, all based on the original Rathbone and Yates charts, like the one illustrated in
Appendix B. Where vibration severity is represented with constant velocity over most of
the frequency range, and constant displacement at the lower end of the range and constant
acceleration at the higher end. Using dimensional analysis that for a given geometric
shape, Rathbone argued that an object, regardless of size, vibrating at the same velocity
would have the same stress in a given mode at its resonance frequency [1].
German Engineers’ Association (Verein deutscher Ingenieure (VDI)) produced in 1957, a
set of criteria under VDI-2056, that took into consideration machines sizes and supports
and adjusted the vibration levels accordingly. Generating four different machine classes as
listed below,
• Small, on a rigid foundation
• Medium, on a rigid foundation
• Large, on a rigid foundation and
• Turbomachines, considered large on soft foundation
and in 1974, ISO incorporated these recommendations into their standard under the name
ISO 2372, later replaced by ISO 10816, as illustrated in Appendix A.
Known strategy in real time monitoring is to measure the vibration overall RMS value,
usually measured with velocity sensors since its more likely that any changes at any
frequency will affect the RMS value more than otherwise. However, monitoring the
frequency spectra, rather than the RMS value, is more likely to detect changes on whatever
frequency they occur. Known cases, see chapter 4 in [1], show that significant changes in
individual frequency components affect the overall RMS value very little if something,
except in the very last stages of the failures. Hence, its recommended to monitor changes
in the vibration spectra, instead of the RMS level, however this can cause additional
problems e.g. faults can occur over very wide frequency range, fluid film bearings can
extend as low as 40% of the shaft speed and up to as high as orders of 1000 or more for
rolling element bearings [1].
Direct digital frequency spectrum comparison is not advisable, because of undersampling
of discrete frequency peaks, where small changes in speed can cause the frequency peaks
to vary as much as 10dB or higher, since the change in speed is not enough to shift the
frequency peaks from one line to another. Hence, if a direct comparison is made it can
seem like there has been a change in the spectrum, even though the peak values look the
same [1]. This is illustrated in Figure 43, where a comparison of frequency spectra from a
REB on a centrifugal blower which is in unchanged operational condition and running at
78
constant speed. The spectrums look really similar but when compared directly it is seen
that the difference can vary up to 21.25 dB.
Figure 43: Comparison of two spectra, with direct digital comparison, with no change in
operational condition.
This is avoidable by making the comparison with mask spectrum obtained from the
reference spectrum, by displacing it to each side and taking the envelope. Another way to
obtain the mask spectrum is to take the upper envelope of all the spectra over a long
period, where every minor load and speed variations have been accounted for but not so
that the operational condition has changed. This method is most suitable for permanent
monitoring systems where the possibility to collect large amounts of data is at hand.
Referring to section 4.2 in [1] for more detailed discussion about the use of frequency
spectrum.
In section 3.2, common fault and their corresponding vibration spectrums are discussed,
for the equipment studied in the case study of this thesis.
4.2.2 Diagnostic Techniques
There are numerus techniques used when performing fault diagnostic on rotating
machinery e.g. Harmonic and sideband cursors, spectral kurtosis, kurtogram, spectrogram,
and envelope analysis, just to name few.
Harmonics and Sidebands
When harmonic and sideband cursors are added to the diagnostic process the capability
increases greatly of the frequency analysis. Harmonic cursors indicate all members of
specific harmonic family, usually with fine resolution, and the diagnostic capability
depends on the fact that the harmonics are precise integer multiples of the shaft frequency.
Harmonic spacing accuracy is in proportion to the highest order located and when zooming
in the high frequency range, N-th harmonics of the same spacing can then be selected
simultaneously and accuracy increases in proportion to N. Sideband cursor indicates a
family of sidebands with fixed spacing around specific carrier frequency, and since it is not
bound to pass through zero frequency the accuracy isn’t the same as the harmonic’s cursor.
However, the same rules apply regarding improving the accuracy when simultaneous
selecting the number of sidebands in the same family. Harmonic and sideband cursors are
extremely useful when conducting frequency analysis on gears where it is possible to
blindly determine the number of teeth on a gear pair, as long as the gear has a hunting
tooth design, meaning there are no common factors between the number of tooth in the
gears [1].
79
Spectral Kurtosis
Spectral kurtosis (SK) is a powerful tool to determine and characterizing the frequency
band which contains a transient signal, it was originally based on the Short Time Fourier
Transform (STFT) for measuring the impulsiveness of a vibration signal as a function of
frequency. The use of SK was later developed to distinguish between sinusoidal signals
and very narrowband noise signals, e.g. faults like small spalls on rolling elements generate
weak impulses that get masked by surrounding noise, usually with SK values of -1 and 0
[24]. The use of SK for bearing fault detection was first introduced by Jerome Antoni in
[25], and also in [26] along with Robert B. Randall.
In equation (45) Y(t) represents a vibration response from a rolling element bearing, where
g(t) is a series of impulse responses, excited by impulses X at time τk. [1]
𝑌(𝑡) = ∑ 𝑔(𝑡 − 𝜏𝑘)𝑋(𝜏𝑘)
𝑘
(45)
For each frequency instance, the kurtosis is calculated as,
𝐾(𝑓) =
⟨𝐻4(𝑡, 𝑓)⟩
⟨𝐻2(𝑡, 𝑓)⟩2− 2 (46)
where H(t, f) is the STFT obtained by shifting a time window along the record, or the
amplitude envelope function where its square is the power spectrum values at each position
along the record. The resulting kurtosis is dependent on which window length is selected
for the STFT and it must be shorter than the spacing between the impulses but longer than
each individual pulse to obtain maximum value of kurtosis. The process of calculating the
kurtosis with STFT is shown graphically in Figure 44, where a simulated vibration signal
from a REB with localized fault on inner raceway is displayed.
Figure 44: The process of calculating the Spectral Kurtosis of a simulated bearing
vibration signal with localized fault on inner raceway.
80
For further discussion on SK and the effect the window selection has on the result is
studied in great detail in [26], where to short window gives too much smoothing on the
spectral kurtosis graph and to long window reduces the maximum calculated kurtosis,
because of bridging between pulses.
Kurtogram
Faulty bearings generally produce impulsive vibration signal with high kurtosis in the
frequency band where the impulsiveness is dominant and usually very low kurtosis where
the frequency spectrum is dominant with stationary vibration components. This
information has been commonly used to filter out the part of the vibration signal that has
most impulsiveness [1]. Jerome Antoni and Robert B. Randall showed in [26] that the
optimum Wiener filter is the square root of the SK and that optimum matched filter
operates as a narrowband filter where the SK is highest. Individual cases are often very
different from one another, even if they are almost identical, so the best solution can vary
with the bandwidth and the center frequency chosen for the filter. In [26] the optimal
combination is displayed on a graph called the Kurtogram.
Computing the full kurtogram can require large computer memory so numerus, more
effect, methods have been proposed. The fast kurtogram was proposed by Antoni in [27]
which is based on series of digital filters instead of STFT. Where the most recommended
method is using the so called 1/3-binary tree [1].
Figure 45 shows the fast kurtogram for measurement number 2150 for bearing 3 from the
experimental vibration dataset number 1, provided by the Center for Intelligent
Maintenance Systems (IMS). The kurtogram was calculated using MATLAB®, it shows
how spectral kurtosis varies with window length and the frequency band, as mentioned
earlier. In this case the bearing had developed inner raceway fault and from the kurtogram
it is seen that the maximum SK is when window length is 32 and center frequency is at
8.64 kHz with bandwidth of 0.64 kHz.
Figure 45: Fast Kurtogram for measurement number 2150 of bearing number 3 from the
IMS-Dataset 1.
81
Figure 46 illustrates how the SK changes when calculated using the optimal bandpass
filtering parameters found with the kurtogram, the maximum kurtosis changes from 2.08 to
6.42. The kurtogram is commonly used to find optimal bandpass filter parameters for the
envelope analysis to withdraw the signals impulsive components.
Figure 46: Comparison on calculated SK with optimal bandpass filter and with no filter.
4.2.3 Prognosis
One of the main benefits from condition monitoring, besides of the fault detection and
diagnosis, is the ability to obtain reliable prediction as to how long equipment is able to
operate safely and reliably. The definition on equipment’s end of life is when it is no
longer able to operate as it was designed for, and to determine remaining useful lifetime
(RUL) for systems or individual components is the basis of prognosis [1].
Aiwina Heng stated in [28], that condition-based prediction is categorized into two main
methods, physics-based, and data-driven. Where physics-based method requires model of
the failure modes, either physical or mathematical, and then measurements to obtain
indication on the extend the failure mode has progressed. The data-driven method is based
on deriving failure models from measurement data, using statistical processing, usually
with number of historical cases [1].
Fault Trending
Often machinery or individual components of larger systems behave rather predictively,
and failures do proceed in that manner, hence fault trending has been used successfully for
a long time. As mentioned in section 4.2.1, frequency spectrums are commonly compared
to a reference spectrum, obtained when machine is new, to see if notable difference has
occurred. The spectrums are often expressed as constant percentage bandwidth (CPB)
spectra, where the frequency scale is logarithmic and the vibration amplitude (acceleration
or velocity) is expressed on a logarithmic or decibel scale. This strategy could be
considered as a physics-based model method and over the year’s maintenance specialists
have gained valuable experience that has been incorporated into known standards, like ISO
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10816. It has been shown that a 6-8 dB change in vibration level is considered significant
and a 20 dB change is serious [1].
There are some situations where the 20 dB change is allowed, e.g. changes at high
frequencies in CPB spectra when fault starts to develop in rolling element bearings (REB).
When REB are new or in perfect condition, they generate very little vibration and it is just
as a fault start to develop it starts to generate measurable vibration. Therefore, the first 10
to 20 dB change in vibration is considered not critical. Change in dB can vary with the
initial noise level and therefore it is considered machine and measurement point dependent,
and it is advisable to use the 20 dB change conservatively and as a guide, where further
adjustments need to be made on tolerances, based on experiment and a given situation [1].
When trending vibration parameters, like overall vibration level, one must decide which
type of curve should be fitted to the data to predict the development of the fault and
determine when actions are needed. If linear trending is used the assumption is made that
the fault development causes uniform rate change in severity. Linear trending may not
always be suitable since some faults have feedback effect, leading to an increase in
deterioration rate as a fault develops e.g. with increasing wear of gearteeth increases the
dynamic load on the teeth, resulting in an increased wear rate. For these cases it should be
considered to use exponential curve to fit the data. Experience has shown that it’s not
recommended to fit polynomials to the data, unless there are physical reasons to assume
that the fault should evolve in that manner or other information indicate that it’s suitable
[1].
In practice it can variate how companies decide how their condition monitoring system
trends machines or system components, all based on their importance, cost, and other
factors that companies decide. Trending single frequency parameters e.g. shaft speed is a
common strategy and used all over the world. Sometimes trending more than one
frequency component can give better results, usually done in the upper frequency ranges,
where there are no longer individual harmonics isolated in a single CPB band [1].
Vibration signals impulsiveness is commonly used as a condition indicator for REB and
gears since localized faults in them is characterized by their generated impulsive
components in the vibration signal. Trending these impulses is therefore a widely used
technique to establish a good measure on faults severity. Measuring the crest factor is one
of technique to measure the impulsiveness in a vibration signal, however it is often
unreliable measure since the peak value varies a lot depending on the signals section
analyzed. Another technique to measure signals impulsiveness is kurtosis, which is more
stable than the crest factor since it is averaged over a number of impulses contained in the
vibration signal [1].
In Figure 47 an example is illustrated on how the kurtosis increases over a bearings
lifetime, where it shows steady measurements when bearings condition is acceptable, but
increases fast once fault starts to evolve. This particular data was obtained from the IMS
dataset number one, bearing number three.
83
Figure 47: Trend of kurtosis for bearing 3 from IMS dataset no.1, inner raceway fault
develops at end of lifetime.
Condition Indicators
The construction of a condition indicator (CI) is important task when performing an
estimation on a bearing remaining useful life (RUL). There are multiple methods used to
establish appropriate CI, all from types like the overall RMS value to complex neural
network-based CI.
Based on their nature common condition indicators are separated into three main
categories, time-domain CI (TD-CI), frequency-domain CI (FD-CI), and time-frequency-
domain CI (TF-CI).
Time-Domain CI are e.g. the overall RMS-value, the mean amplitude, Peak-to-Peak
amplitude, and the Crest factor (see section 3.1.1 for their definition). The standard
deviation (STD) in a signal, or higher-order moments of such as Skewness and Kurtosis
are also common time-domain CI.
Where standard deviation is calculated as,
𝑆𝑇𝐷 = √1
𝑁 − 1∑(𝑥𝑖 − )2
𝑁
𝑖−1
(47)
the signal skewness is calculated as,
𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 =
1𝑁
∑ (𝑥𝑖 − )3𝑁𝑖=1
(√1𝑁
∑ (𝑥𝑖 − )2𝑁𝑖=1 )
3 (48)
84
and the Kurtosis is calculated as,
𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠 =
1𝑁
∑ (𝑥𝑖 − )4𝑁𝑖=1
(1𝑁
∑ (𝑥𝑖 − )2𝑁𝑖=1 )
2 (49)
where, xi are the measured values at each time instance and is the mean value of all those
values and N is the number of values.
It has been shown that the Kurtosis of a bandpass filtered signal, with optimal filter (as
described in section 4.2.2), follows similar trend as cumulated oil wear debris and hence
has strong potential for estimation of RUL [1].
Frequency-Domain CI are e.g. the mean frequency and the mean-peak frequency.
Where the mean frequency is calculated as,
𝑓𝑚𝑒𝑎𝑛 =
∑ 𝐼𝑖 ∗ 𝑓𝑖𝑁𝑖=𝑜
∑ 𝐼𝑖𝑁𝑖=0
(50)
where N being the number of frequency bins in the data, fi is the frequency of spectrum at
each bin and Ii is the intensity in dB of spectrum at each bin.
The mean-peak frequency is established by extracting features from signals spectrogram,
where peak frequency at each time instance is defined as,
𝑓𝑝𝑒𝑎𝑘 = 𝑎𝑟𝑔𝑚𝑎𝑥𝜔𝑃(𝑡, 𝜔) (51)
where P(t,ω) denotes the signal spectrogram, and the mean-peak frequency is the averaged
of the peak frequencies, defined as,
𝑓𝑚𝑒𝑎𝑛−𝑝𝑒𝑎𝑘 =
1
𝑁∫ 𝑓𝑝𝑒𝑎𝑘 (𝑡)𝑑𝑡
𝑁
0
(52)
Time-Frequency-Domain CI are e.g. the spectral Kurtosis and spectral entropy. See
section 4.2.2 for spectral Kurtosis definition.
The spectral entropy (SE) is a measure on signals spectral power distribution, it is defined
as,
𝐻(𝑡) = − ∑ 𝑃(𝑡, 𝑖)𝑙𝑜𝑔2𝑃(𝑡, 𝑖)
𝑁
𝑖=1
(53)
where P(t,i) is the probability distribution at time t.
85
Advanced Prognosis
Prognosis is divided into two main categories, as mentioned earlier, physic-based, and
data-based and then there is a combination of the two called Hybrid model-based method.
Where the prognostic process is a mixture of reliability and condition-based estimates,
where the focus shifts, during the lifetime of the equipment, from former to the latter [1].
J. W. Hines and A. Usynin proposed great strategy for general prognosis in [29], where the
method is based heavily on experience from the nuclear industry amongst other things. The
strategy is illustrated in Figure 48, where the type I prognostics uses actual failure history
to obtain statistical information about probability of component failure. It usually applies
to all components of the same type, since it gives average results for all components under
average operating conditions, however it leads to an uncertainty for current component [1].
The second type prognostics factors in things that effect machine or components lifetime
e.g. load, running speed, how many running cycles component has served, operational
temperature, environmental conditions such as cleanliness, etc. It is possible to monitor all
of these things for every component as an individual or as a whole, and by adding these
parameters into prognostics the uncertainty of the RUL prediction is reduced [1].
The third and final type of prognostics also includes measures on how components perform
to improve the estimation results on the RUL, this approach is considered the best,
especially in later stages on components lifetime, when deterioration has started to effect
the measured parameters [1].
Figure 48: Prognostic method as proposed by J.W.Hines & A. Usynin in [29]
87
5 Case Study at Norðurál Aluminum
Plant
In this chapter the case study of this thesis will be discussed and the process of selecting,
designing, and building the vibration measurement- and analysis system used for the case
study.
Norðurál is an aluminum plant located at Grundartangi, where the production takes place
in 520 pots in four pot rooms. Large fume treatment plants (FTP’s) are located between the
pot rooms and their main purpose is to reduce the amount of emission released into the
atmosphere e.g. fluoride gases and carbon dioxide. There are four FTP’s operating at the
plant, but the focus in this thesis was on FTP-1, although FTP-2 was also included during
the vibration measurements to get some comparison between the two, due to their
mechanical similarity.
Figure 49 illustrates an overview of the plant, where the location of the pot rooms and
fume treatment plants have been marked. FTP-1 serves pot rooms A and B and FTP-2
serves lower part of pot rooms C and D.
Figure 49: Norðurál aluminum plant and the location of the pot rooms and FTP’s.
The company has been having unusual problems regarding one of the four main fans in
FTP-1, where bearing lifetime have been well below designed lifetime on main fan number
two. Resulting in unreliable operation since an unexpected shutdown of one of the fans
reduces the systems capability to serve its purpose. Although it is possible to operate the
FTP without one fan running it is not ideal in the long run. It leads to increased load on the
remaining fans if the system is to uphold its capacity to treat the plant’s emission.
The objectives of this study are divided into two parts, first part is to apply Failure Mode
and Effect Analysis (FMEA) on fan 2 in FTP-1, to try to identify the cause of the reduced
bearing lifetime. The second part is assessing the current condition of the main fans rolling
element bearings (REB) in FTP-1 and FTP-2 and estimate the remaining useful life (RUL)
of one of the plummer block bearings on main fan 2 in FTP-1.
88
5.1 Fan Setup and History
FTP-1 and FTP-2 are both installed with four parallelly lined centrifugal fans, with rated
power around 900 kW each, applying ventilation from the pots in the pot rooms. The fans
setup is almost identical in both plants, where all components are similar. The fans are in
constant operation all year around, excluding their downtime cause of maintenance issues,
and it is essential that their operation is reliable and safe. If the fans are not operating
consistently the FTP’s efficiency reduces, resulting in an increase in emission release from
the factory. The company has committed itself to operate under strict environmental
regulation regarding emission release, hence it is crucial that the FTP’s are operating
smoothly and without problems.
5.1.1 Components and Operational Conditions
The fans were manufactured by ABB Fläkt Industri AB, they are of backward curved blade
type HACB-200-251-X4-1-2 which is suitable for handling gas containing small amounts
of non-adhesive dust. The fans have an overhung impeller and are mounted on a rigid
foundation with direct drive connection, via a flexible coupling. The impellers are of
welded design and are both statically and dynamically balanced when new. Figure 50
illustrates a sideview of one of the fans and a list of its major components.
Figure 50: Sideview of one of the centrefugal fans and a list of it’s major components
The fans bearings are fitted to the driveshaft and are supported by plummer block type
bearing housings and designed with grease lubrication. Two types of bearings are used in
this setup, excluding the internal bearings of the electric motor which will not be discussed
further in this thesis. The bearing closer to the electric motor, denoted as Fan DE (Drive
End) bearing, is a spherical roller type and the bearing closer to the impeller, denoted as
Fan ND (Non-Drive end) bearing, is a CARB toroidal roller type. The spherical roller
bearing is designed to take high loads, both radial and axial, and has the ability to
accommodate for misalignment. The CARB bearing is designed to take loads exclusively
in radial direction. Table 10 and Table 11, in appendix C, lists all major parameters, for
both bearings and the lubricant, gathered by using the force estimation illustrated in Figure
51 as design inputs in the SKF® Bearing Select software. Where interestingly it shows that
89
the estimated bearing life is over 200k hours for both bearings, the manufacturer however
notes that for calculated bearing life over 100k hours, other failure modes than the ones
included in the current rating life model will dominate and limit the life of the bearing.
Both bearings are mounted on the driveshaft along with adapter sleeves, with so-called
SKF Drive-Up method [30], where the bearings are pressed onto the adapter sleeves with
HMV hydraulic nut. The bearings are fitted inside the housings along with the locating
rings, which fix the bearings position inside the housing. Both bearings are grease
lubricated with SKF LGHP2 high performance grease. Figure 51 illustrates how the setup
looks like with the driveshaft fitted with the bearings, adapter sleeves, and along with their
housings and the impeller.
Figure 51: Current bearing setup and impeller for fans in FTP-1 and FTP-2.
The bearing housings, both Fan DE and ND, are mounted with thermo probe and vibration
accelerometer which monitor temperature and vibration in the bearing housings. The
measured values are logged into the monitoring system for the plant, called SCADA
(Supervisory Control and Data Acquisition). The SCADA system monitors the measured
values from the thermo probe and the vibration accelerometer including other parameters
e.g. electric motor amperage, and gives indication when values exceed predetermined
limits. The vibration measurements are logged as an averaged RMS-value for a given
bearing, commonly as a one-minute average. The SCADA system has the ability to shut
down any of the fans if operational condition gets dangerous because of exceeding
temperature or vibration, however it has its limitations when it comes to advanced
vibration analysis.
Figure 52 illustrates roughly how the force distribution is along the driveshaft on main fans
1 and 2 in FTP-1, where it shows that the Fan DE bearing takes a small axial load and very
small radial load, and the Fan ND bearing receives exclusively small radial load.
According to SKF® the bearings are designed to receive loads well above these values, as
listed in Table 10 in appendix C.
As mentioned earlier the fans are running constantly all year around at fixed speed, approx.
993 rpm, and at load that varies very little, since the ventilation from the pot rooms is a
constant process where aluminum production is a process that cannot stop at any time.
90
Figure 52: Rough estimation on force distribution along the driveshaft and the bearings.
5.1.2 Plummer Block Bearings Maintenance History
The maintenance strategy practiced at Norðurál is somewhat a combination of all three
main maintenance strategies generally practiced. Where breakdown maintenance is often
used on noncrucial and considerably cheap equipment and preventive maintenance is
practiced on machines and components that have somewhat known lifetime. Predictive
maintenance is used on larger and more complex and expensive machines and components,
where possible breakdown can interrupt or effect the production or threaten the safety of
personnel. The technical department at Norðurál uses a very powerful maintenance
software called SAP® (Systems, Applications & Products in data processing) to register
maintenance history and spare part inventory, and plan future work.
The main fans in the fume treatment plants are one of the crucial components in the
operation of the FTP’s and when one of them breaks down it is considered a major
problem and needs immediate attention. Therefore, the fans are under regular planned
maintenance work e.g. visual inspection, grease change, and grease injection at certain
intervals. As mentioned earlier, are the main fans plummer block bearings installed with
condition monitoring equipment, where temperature, vibration, and motor amperage are
monitored constantly.
Table 12, in appendix C, lists bearing- and grease change intervals for the main fans in
FTP-1 & 2 from the year 2008. Table 3 lists up all the bearing changes that have been
performed in FTP’s 1 & 2, where interestingly it shows that main fan 2 has by far the worst
track record when it comes to plummer block bearing life. However, there have been
bearing change on main fan 1 three times, latest in February 2019, and it has currently been
having some operational problems, according to maintenance personnel, and it is expected
that bearing change will be performed in the upcoming weeks/months.
Table 3: Date of bearing changes for the plummer block bearings on main fans in fume
treatment plants 1 and 2, with bearings running hours at each time.
Main Fan No.
2008-05-10 87,144 2008-03-18 85,872 2012-09-24 125,496 (66,768) 2013-11-25 135,744 (56,520)2010-03-12 16,104 2013-04-02 44,1842019-02-18 78,360 (10,656) 2017-09-25 39,288
2017-12-11 1,8482018-02-17 1,6322019-07-05 12,072 (7,368)
FTP-2 2015-04-27 86,808 (44,088)
FTP-1Bearing Change
Date & Running
Hours (current)
4321
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Main fans 1-3 in FTP-1 were initially started in 1998 and main fan 4 in the year 2000 and
according to Table 3 were the initial bearings in FTP-1 in operation from 10 to 14 years,
which is considered well acceptable bearing lifetime. Barely two years from the first
bearing change, the bearings were changed again on main fan 1 which is unacceptable
lifetime. However, there has not been any bearing change since then, giving the current
bearings a lifetime of approx. 10 years which is considerably fair, although there is a
bearing change imminent. Since the initial bearings were changed, on main fan 2, in the
year 2008 there have been five bearing changes done which is extremely poor durability
and is one of the main reasons for this thesis.
The fans in FTP-2 were initially started in the year 2005 where all the initial plummer
block bearings are still in operation except the ones on main fan 1, which were replaced in
the year 2015 after approx. 10 years in operation. The lifetime of current bearings in FTP-2
is well acceptable and does not cause any concerns regarding their operation.
The problem that the bearings in main fan 2, in FTP-1, has been causing random vibration
spikes now and then and causing an unusual increase in operating temperature. These
vibration spikes have caused the SCADA system to automatically shut down the main fan.
Figure 53 illustrates how the inside of the plummer block bearings has attended to look
like before the maintenance personnel perform bearing grease change. It shows that the
grease is completely damaged and looks like it has overheated.
Figure 53: Sooted grease in main fan 2 plummer block bearings, in FTP-1.
During bearing grease change the maintenance personnel have been encountering some
metal debris and metal threads in the grease, which look like missing parts from the
locking washer and/or the bearing cage. Figure 54 illustrates some of the metal debris,
including some damages on the adapter sleeve and the locking washer. The metal threads
appear to be parts that have been scraped of the bearings cage or its inner raceway,
although it is not able to be certain from these pictures.
92
Figure 54: Damages found in main fan 2 Fan DE bearing, during grease change.
It displays in these pictures that there is a serious problem when it comes to the durability
on the Fan DE and Fan ND bearings on main fan 2 in FTP-1 and the current operating
condition is unacceptable and it’s causing the company great concerns.
5.2 Vibration Measurement and Analysis System
Selecting appropriate equipment for the vibration measurement part of this thesis was one
of the first steps taken. Some of the equipment was designed and build especially for this
thesis and some parts were bought from known suppliers e.g. accelerometers and analog to
digital converters. Vibration analysis software was designed using MATLAB®, where a
graphical user interface (GUI) was built to help performing the analysis part of this thesis
as well as to make it more user friendly.
It was decided to use two sets of vibration measurement equipment, where the first set
(denoted as localized equipment) was placed beside main fan 2 in FTP-1 to take one
vibration measurement every 24 hours on the plummer block bearing housings with two
accelerometers on each (axial- and radial-direction). The second set (denoted as portable
equipment) was thought of as a portable unit to be able to perform vibration measurements
on all other main fans, also with four accelerometers. Although both sets have the same
purpose, there is a difference between the two where the portable equipment is much more
powerful unit with higher resolution and sampling rate.
It was decided to use accelerometers instead of velocity- or displacement transducers. The
velocity transducer is not suitable for the advanced vibration analysis performed, since the
frequency range of velocity transducers is not as wide as the range on accelerometers. The
use of a proximity probe (displacement transducer) is not suitable either, since the probe
requires customize mounting on the plummer block bearing housing and therefore was
ruled out as an option.
93
5.2.1 Localized Equipment
During the design process of the localized equipment it was decided to keep its structure
basic to keep the cost down, since there were limiting funding for this project. Decision
was made to build the analog low-pass filters, with amplification, from scratch, but other
components like accelerometers and ADC’s were bought from commercial manufactures,
along with parts used to assemble the equipment e.g. casings and other structural parts.
The basic design parameters that the localized equipment needed to fulfill were,
• Low-pass filters cut-off frequency to be 1000 Hz, since this frequency range covers
all the bearing fault frequencies and their first few harmonics.
• Sample frequency to be 2500 Hz, with number of samples per measurement 2500,
to keep sampling frequency 2.5 times the low-pass frequency (to avoid aliasing).
• Measure with four accelerometers simultaneously with 24-hour interval (radial and
axial directions on each plummer block bearing housing).
Low-Pass Filter and Amplifier Design and Construction
For this project it was decided to design and build a fifth-order Butterworth low-pass filter
with 22 dB signal amplification, since its rather easy to build and provides reasonably good
precision. The 22dB amplification was included in the design because of the weak electric
signal from the accelerometer. Analog low-pass filters are electronic circuits with resistors,
capacitors, and amplifiers connected in a certain way, where the selection the components
size controls the function of the filter. A fifth-order Butterworth filter is basically
constructed with three Butterworth filters, with one first-order and two second-order filters
connected in series. Figure 55 illustrates a basic electrical circuit for a fifth-order
Butterworth low-pass filter without any amplification.
Figure 55: Electrical circuit for a fifth-order Butterworth filter without any amplification,
where R and C denotes the resistors and the capacitors.
To determine the suitable size of the electrical components so the filters cut-off frequency
is at approx. 1000 Hz its crucial to set up a transfer function for the filter. For convenience
MATLAB® was used to determine the filters transfer function, giving the following,
𝐻(𝑠) =
1
𝑠5 + 3.2361𝑠4 + 5.2361𝑠3 + 5.2361𝑠2 + 3.2361𝑠 + 1 (54)
94
for simplification, the transfer function is factored so it is constructed of one first-order and
two second-order components, which represent the first- and second-order filters
connected in series, resulting with
𝐻(𝑠) =
1
(𝑎1𝑠 + 1)(𝑠2 + 𝑎2𝑠 + 1)(𝑠2 + 𝑎3𝑠 + 1) →
𝐻(𝑠) =1
(𝑠 + 1)(𝑠2 + 1.618𝑠 + 1)(𝑠2 + 0.618𝑠 + 1)
(55)
Sallen-Key method [31] was used when designing the filter, where the constants a are
utilized from equation (55) to establish the size of the electrical components to obtain
desired cut-off frequency.
For simplification it was decided to use 1 kΩ resistors for all the resistors in the electric
circuit, that way only one variable (capacitors size) controls the filters cut-off frequency.
The capacitor size for the first-order filter was calculated as,
𝐶1 =
𝑎1
2𝜋 ∗ 𝑓𝑐 ∗ 𝑅1=
1
2𝜋 ∗ 1000 ∗ 1000= 159 𝑛𝐹 (56)
where fc is the cut-off frequency and R1 is the size of the resistor.
A different approach is used to determine the capacitors sizes for the second-order filters.
The transfer function for the second-order filters is,
𝐻(𝑠) =
𝑘𝑅2𝑅3𝐶2𝐶3
⁄
(𝑠2 + (1
𝑅2𝐶2+
1𝑅3𝐶2
+1
𝑅3𝐶3−
𝑘𝑅3𝐶3
) 𝑠 + 1) (57)
which has a general form,
𝐻(𝑠) =
𝑘𝜔2
(𝑠2 + (𝜔0
𝑄 ) 𝑠 + 𝜔02)
(58)
if k and ω0 are set as 1, the transfer function is rewritten as,
𝐻(𝑠) =
1𝑅2𝑅3𝐶2𝐶3
⁄
(𝑠2 + (1
𝑅2𝐶2+
1𝑅3𝐶2
) 𝑠 + 1) (59)
for simplification, the resistors values are set as 1,
𝐻(𝑠) =
1
(𝑠2 + (1𝑄) 𝑠 + 1)
=1
(𝑠2 + (2𝐶2
) 𝑠 +1
𝐶2𝐶3)
(60)
95
with this information the capacitors size ratios could be established, for both second-order
filters, with the following calculation,
𝐶2 = 2𝑄 , 𝐶3 =1
2𝑄 , 𝑎2 =
2
𝐶2 𝐶4 = 2𝑄 , 𝐶5 =
1
2𝑄 , 𝑎3 =
2
𝐶4
𝐶2 =2
𝑎2=
2
1.618= 1.236 , 𝐶3 =
1
2𝑄=
1
𝐶2=
1
1.236= 0.809
𝐶4 =2
𝑎3=
2
0.618= 3.236 , 𝐶5 =
1
2𝑄=
1
𝐶4=
1
3.236= 0.309
to establish the real sizes of the capacitors the C values are multiplied with,
1
2𝜋𝑓𝑐𝑅=
1
2𝜋 ∗ 106
Table 4 lists up the calculated sizes of the capacitors and the real sizes used to construct the
filter, along with the sizes of the resistors. The real sizes are little bit different from the
ones calculated, this was done to simplify the design process by picking standard size
capacitors closest to the calculated values.
Table 4: Calculated and used sizes of capacitors.
Figure 56 illustrates how the electrical components are arranged, along with two added
resistors which alter the filters function, giving it 22 dB amplification.
Figure 56: The electrical circuit for fifth-order Butterworth low-pass filter with 1000 Hz
cut-off frequency and 22 dB amplification.
Next step was to design a power circuit that supplies an accelerometer and the low-pass
filter. Figure 57 illustrates a power circuit for a single accelerometer and one filter. The
circuit is constructed with numerous components like, on/off switch, LED-diode, constant-
current diode, input connection for an accelerometer and an output connection from the
filter (to the AD converter), and a power supply.
Capacitor [C] 1 2 3 4 5
Calculated Size [nF] 159 129 196 49 515
Used Size [nF] 150 133 183 47 503
Resistor [R] 1 2 3 4 5
Used Size [ Ω] 1000 1000 1000 1000 1000
96
Figure 57: Power circuit for a single accelerometer and a low-pass filter.
Constant-current diode’s purpose is to regulate the current flow to the accelerometer
(approx. 4.8 mA) when the 22μF capacitor works as a gate that stops the current flow from
the power supply to the filter, but allows the filter to receive the voltage signal from the
accelerometer. The LED-diode function is simply to give indication when the circuit is
switched on.
Next step was to test the functionality of the filters and confirm its cut-off frequencies.
Where the standard procedure for defining the cut-off frequency was used, its where the
signals power has been halved or decreased by 3 dB. The theoretical response of the filters
was calculated using MATLAB® and Bode diagram used to draw up the response to
compare it to the real response. Figure 58 illustrates how the theoretical response is for the
designed filters, it shows that the theoretical cut-off frequency is 924 Hz which is
understandable since the electrical components used are not exactly the same size as the
ones calculated during the design process.
Figure 58: Filters theoretical respone displayed on Bode diagram.
The real filters responses were established by connecting the filters to a power source and
use adjustable wave generator as an input signal. The output signal was then measured
using an oscilloscope and the frequency response plotted using Excel®. The results are
illustrated in Figure 59, where it shows that the frequency responses are very similar for all
the filters and the cut-off frequencies results are all very acceptable.
97
Figure 59: The real frequency response of the filters, measured with the oscilloscope.
It was decided to build a compact filter unit with the option to connect six accelerometers
to it, since the objective was to measure with four accelerometers simultaneously, with the
option to add two more if necessary. Table 13 in appendix D lists up all major components
used to build the compact filter unit. Figure 60 illustrates how the filter unit looks like,
where two connection boards with three low-pass filters each are stacked on top of each
other and connected to two 12-volt batteries that are connected in series. The filters and the
batteries, along with the power circuit are placed inside a plastic box, with six BNC-
connection on each side for connecting the accelerometers and the output to the AD
converter.
Figure 60: The compact filter unit, with six 1000 Hz low-pass Butterworth filters.
98
Analog to Digital Converter
The analog to digital converter that was used for the localized equipment was bought from
National Instruments® and is illustrated on Figure 61 along with its dimensions and key
specification, Table 14, in appendix D, lists up further specifications.
Figure 61: NI USB-6000 ADC dimensions and key specifications.
This ADC fulfills the requirements needed for the localized equipment, just barely though
since it has a maximum sample rate of 10 kS/s aggregated amongst all used inputs. There
were four accelerometers used for this setup, resulting in a maximum sample rate of 2500
kS/s per transducer. It would have been good to have higher input resolution but was
acceptable because of cost concerns.
Accelerometers
The accelerometers used for the localized equipment are from STI-Vibration-Monitoring
Inc. named CMCP-1100 standard. These accelerometers come fitted with 5m long integral
cable and a threaded mounting port. Figure 62 illustrates the accelerometer’s dimensions
and key specifications, Table 15 in appendix D lists up more detailed specifications about
theses accelerometers.
Figure 62: CMCP1100 accelerometer dimensions and key specification.
Next step was to calibrate the accelerometers in function, one at a time, connected to the
low-pass filters and the ADC to establish the amplitude’s factor for each of them. This is
important procedure because the amplitude factor scales the measured value, so it matches
the real value. Vibration calibrator from Gilchrist Technology Inc. model 4000 was used
for the calibration process, it has an oscillator with adjustable amplitude and frequency.
99
The calibration procedure was as follows,
• Accelerometers mounted with magnetic base onto the oscillator
• Accelerometers, filter unit, ADC, and laptop (with control software) all connected
• Oscillator vibration preset with amplitude at 5 mm/s2 and frequency at 50 Hz
• Measurements taken at these frequencies, 50, 75, 100, 200, 500, and 750 Hz (with
amplitude declining with increasing frequency)
• Amplitude factor calculated for each frequency and then averaged
The amplitude factors S are listed in Table 5 below, they were calculated for each
frequency interval i and then averaged as,
𝑆𝑓𝑟𝑒𝑞𝑖
=𝑂𝑠𝑐𝑖𝑙𝑙𝑎𝑡𝑜𝑟 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒𝑓𝑟𝑒𝑞𝑖
𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒𝑓𝑟𝑒𝑞𝑖
(61)
𝑆𝑡𝑜𝑡𝑎𝑙 =
𝑆𝑓𝑟𝑒𝑞_1 + 𝑆𝑓𝑟𝑒𝑞_2 + 𝑆𝑓𝑟𝑒𝑞_3 + 𝑆𝑓𝑟𝑒𝑞_4 + 𝑆𝑓𝑟𝑒𝑞_5 + 𝑆𝑓𝑟𝑒𝑞_6
6 (62)
Table 5: Accelerometers amplitude factors.
Assembly and Setup
The low-pass filter unit, the Analog-to-Digital converter, and a laptop were mounted inside
electrical cabinet, as illustrated in Figure 63. The accelerometers cables were fitted through
the bottom of the cabinet with waterproof cable glands. Inside the cabinet a multi plug was
fitted for plugging the laptop and filter unit chargers to 230 V.
Figure 63: Localized equipment fitted inside an electric cabinet.
Accelerometer 1 2 3 4
Amplitude Factor 0.240 0.233 0.240 0.227
100
Figure 64 illustrates the localized equipment placed beside main fan 2 in FTP-1, with the
cabinet connected to the factories 230 V power grid. The accelerometers are mounted on
the bearing housings with magnetic bases and placed in axial and radial directions.
Figure 64: Locacalized equipment at site, with electric cabinet connected to 230 V power
grid and accelerometers mounted on bearing housings with magnetic bases.
5.2.2 Portable Equipment
The portable equipment includes high end commercially bought equipment e.g. powerful
dynamic signal analyzer, high frequency accelerometers, low loss coaxial cables, and
threaded mounting components. It was decided to buy very powerful equipment to be able
to perform analysis on the high frequency range to detect early stage bearing faults.
Dynamic Signal Analyzer
Figure 65 illustrates the dynamic signal analyzer and its key specifications. The analyzer is
manufactured by MC Measurement ComputingTM and is a compact unit with very
powerful features for vibration analysis e.g. ADC with 24-bits resolution, sample rate of
105.4 kS/s per channel, and built in high-pass filter and adjustable low-pass filter.
Figure 65: DT9837B dynamic signal analyzer and its key specifications.
101
Accelerometers, Cables, and Setup
The portable equipment’s accelerometers were bought from Wilcoxon Research Inc. and
are compact, high sensitivity, and high frequency accelerometers. Figure 66 illustrates the
accelerometers and their mounting parts along with the cable. These accelerometers have a
frequency range from 2 Hz to 25000 Hz and they are light weight with compact structural
design. They have a threaded mounting hole and coaxial connection with 10-32 threads.
The mounting parts was built specifically for this project, to be able to utilize the threaded
port on top of the bearing housings. The cables are 5-meter coaxial cables with low signal
loss, with one end fitted with BNC connector ( which connects to the dynamic signal
analyzer) and the other with 10-32 threaded connection (connects to the accelerometers).
Table 16, in appendix D, lists up more detailed specification about the accelerometers.
Figure 66: High frequency accelerometer and mounting parts dimensions and setup.
Figure 67 illustrates the accelerometers mounted on the bearing housings, in axial and
radial direction. The threaded holes on top of the bearing housings were used to mount the
accelerometers, these holes are normally mounted with an eyebolt for lifting.
Figure 67: Accelerometers mounted on bearing housings, fitted in the eyebolts threaded
holes.
102
5.2.3 Vibration Analysis Software
To perform vibration analysis on the measurements gathered from the plummer block
bearings, multiple codes were written using MATLAB®. It is a powerful mathematical
computing software developed by MathWorks® Inc. For convenience and more robust
analysis a graphical user interface (GUI) was designed and developed using the GUIDE
user interface environment in MATLAB®. An option to perform measurements directly
using the GUI was also added. Figure 68 illustrates how the GUI lay out looks like.
Figure 68: Graphical user interface for the vibration measurement and analysis software.
Figure 69 illustrates a flowchart on how to perform and save measurements. First step is to
set the measurement sample frequency and how many samples to take. Next step is to
perform the measurement, where the measurement raw data loads in the GUI’s memory.
Next the user can decide to perform vibration analysis of the measurement, referring to
Figure 70, or decide to skip the analysis and name it and save in the database.
Figure 69: Flowchart of measurement and saving process.
103
Figure 70 illustrates a flowchart for the vibration analysis process on a single
measurement. First step is to load a single measurement in the GUI memory, see flowchart
in Figure 71 if two or more measurements are loaded. Amplitude representation
(Acceleration, Velocity, or Displacement), which sensor to examine, and weighing window
(Rectangular, Hanning, or Flat top) are then selected, the user can then choose to scale the
axis on the plots, if desired. Fourier- and Envelope analysis are the two main features in the
GUI to analyze the vibration data. Before the running FFT or Envelope the user can choose
to plot the bearing fault frequencies onto the frequency and envelope spectrums.
Additional feature is included in the GUI to calculate bearing fault frequencies based on
the geometric parameters of the bearing and shaft speed. Setting the order for the band-
pass filter and the method (Hilbert or Demodulation) are additional settings for the
Envelope process. There are four more features available in the GUI to support the analysis
process, these features plot a Kurtogram, the spectral Kurtosis, power spectrum, and the
Spectrogram (which has some additional settings to it).
Figure 70: Flowchart for the single measurement analysis process, (continued flowchart
from Figure 69).
104
Figure 71 illustrates a flowchart for the vibration analysis process for multiple
measurements. They main difference between the multi measurement analysis and the one
with a single measurement is that the multi analysis is mostly thought of as a comparison
between measurements at different time instances. There are two main features for the
multiple measurement analysis, one being a comparison between measurements with FFT
represented either on a waterfall plot (3-dimesional) or conventional frequency spectrum
plot (2-dimensional), and the other one being a condition indicator plotting. The procedure
is similar to the single measurement analysis, where a sensor is chosen, but for the
condition indicator analysis there is no need to choose amplitude representation or
weighting window, after that the axis scales are chosen, if desired.
Figure 71: Flowchart for the multiple measurement analysis process, (continued flowchart
from Figure 70).
Referring to appendix E for more visualization on how to operate the GUI.
5.3 Assessments and Condition Measurements
Next step in this case study was to utilize the techniques discussed in earlier chapters to try
to identify the causes for the problems that Norðurál is having on main fan 2 in FTP-1.
Additionally, to perform vibration measurements and analyze the results and to evaluate
the current condition and establish the remaining useful life (RUL) of the plummer block
bearings on main fan 2 in FTP-1.
5.3.1 Assessing Causes for Short Bearing Life
At appears that the problem occurring on main fan 2 plummer block bearings, in FTP-1, is
isolated to the unusual short bearing lifetime. However, it was decided to perform failure
assessment on the fan setup to try to isolate the cause and to see if there are any combined
effects causing the short bearing life. It was decided to start by performing Failure Mode
and Effect Analysis (FMEA) on the main fan setup.
105
FMEA
The worksheet used for this analysis is similar to the one illustrated in Figure 1, in section
2.2.1, where similar severity and occurrence criteria, as illustrated in Figure 2, and
detection criteria, as illustrated in Figure 3, were used.
In Table 17, in appendix F, the complete FMEA worksheet for main fan 2 in FTP-1, is
illustrated, where the main fan was divided by its main items. The items and their main
functions are, the electric motor which acts as the prime mover, the flexible coupling and
the drive shaft which form the power train, the plummer block bearings that support and
receive axial and radial forces, the impeller which is the driven part, and the foundation
which supports the system as whole.
On next page Table 6 lists up all the failure modes that scored approx. 150 or higher risk
priority number (RPN) (see equation (1) in section 2.2.1) from the analysis. Were all the
effects and causes that scored 200 or more are highlighted. Only one item scored 200,
excluding the plummer block bearings, it was the coupling, for the potential failure mode
where the coupling shatters because of faulty assembly. The risk of this particular failure is
reduced by following assembly instruction and use appropriate equipment to line up the
coupling during the assembly process.
As mentioned earlier, the plummer block bearings are the components that the company
has been having problems with regarding reduced lifetime. There were four potential
failure modes defined for the bearings and the housings. First is damage on rollers,
inner/outer raceways, and/or roller cage where the potential effects being increased
temperature and vibration. The second is structural damage on the housings where the
potential effects are housing and/or fasteners break/crack and increased vibration. The third
one is damage on the adapter sleeve and/or lock ring where the potential effects are
increased vibration and the sleeve/ring breaks. The fourth one is damage on the axial seals
where potential effects are grease contamination and damaged shaft surface.
Both potential effects, increased temperature, and vibration, on rollers, inner/outer
raceways, and/or roller cage have potential causes that have an RPN over 200. Where
increased temperature caused by faulty assembly or reduced grease life score 210.
Increased vibration caused by spalls on the surface of the rollers and/or raceways score
216. Where the risk for the potential causes, for the increased temperature, is reduced by
following assembly instructions and monitor the grease condition regularly. Where the risk
for the potential cause, because of increased vibration, is reduced by monitoring the
vibration level and perform appropriate actions when levels are increasing e.g. greasing
and bearing change.
Same occurred for both potential effects for both the structural damage and damaged
adapter sleeve and/or lock ring failure modes. Where faulty assembly on the bearing
housing scored 270 and increased vibration cause of looseness scored 210. Increased
vibration cause of faulty assembly on the adapter sleeve scored 210, and the effect where
the adapter sleeve and/or lock ring breaks cause of faulty assembly scored 270. The failure
mode for damaged seals did not have any effects that scored over 200. Same applies to the
impeller and foundation although, increased vibration on the impeller cause of faulty
assembly scored 180.
106
Table 6: Failure modes, effects, and causes with highest RPN for main fan 2 in FTP-1,
summ-up from the full FMEA-worksheet listed in Table 17, in appendix F.
The FMEA indicates that the highest risk factors that leads to faulty operation on the main
fan, are often caused by faulty assembly. According to maintenance personnel at Norðurál,
during bearing change and other maintenance work performed on the main fans, all
manufacturers assembly instructions are followed, and certified methods are used when
electric motor, coupling, and driveshaft are lined up. Same applies to the assembly on the
plummer block bearings, where a certified method is used to fit bearings and adapter
sleeves on the driveshaft, so called SKF drive-up method as mentioned earlier. Based on
this information the cause for the short bearing life cannot be pinpointed from the FMEA.
Ite
m
Function
Potential
Failure
Mode
Potential
Effect(s)
of Failure Seve
rity Potential
Cause(s)
of Failure
Occ
urr
en
ce
Current Design
Controls
(Prevention)
Current Design
Controls
(Detection) De
tect
ion
R P
N Recommended
Action(s)
Electric
Motor
Prime
Mover
Electrical
overload
Over
Heating7
Isolation
degrades3
Prevent over-heating,
minmize corrosion, and
physical wear/damage
Through visual
inspection8 168
Set up regular
inspections schedule
CouplingConnect motor
and drive shaft
Structural and/or
fasteners damage
Coupling
shatters10
Faulty
assembly4
Follow assembly
instructions
By double
checking assembly5 200
Make sure assembly
instructions are followed
Drive
Shaft
Deliver torque
to impeller
Structural
damage
Permanent
Bent7
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly5 175
Make sure assembly
instructions are followed
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly6 210
Make sure assembly
instructions are followed
Reduced
grease life5
Monitor grease
condition
With regular
checks6 210
Set up regular
inspections schedule
Increased
clearance5 Monitor vibration
With automatic
measurements6 180
Monitor bearings vibrations
regularly
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly6 180
Make sure assembly
instructions are followed
Spalls on
surface6 Monitor vibration
With automatic
measurements6 216
Monitor bearings vibrations
regularly
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly6 270
Make sure assembly
instructions are followed
Foreign object
impacts4
Use protective
covers
Through visiual
inspection5 180
Make sure foreign objects
cannot impact components,
set up protective covers
Looseness 6Tighten bolts with
appropriate torque
By double
checking torque5 210
Make sure assembly
instructions are followed
High bearing
clearance5 Use certified parts By measurements 5 175
Monitor bearings vibration
regularly
Increased
vibration7
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly6 210
Make sure foreign objects
cannot impact components,
set up protective covers
Adapter sleeve
and/or lock ring
breaks
9Faulty
assembly5
Follow assembly
instructions
By double
checking assembly6 270
Make sure assembly
instructions are followed
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly6 150
Make sure assembly
instructions are followed
Damaged
shaft surface6
Visually examine
part
By double
checking surface5 150
Set up regular
inspections schedule
Material
wear4 Use durable materials
Through visiual
inspection6 168 Use durable materials
Material
fatigue 3
Examine part
before assembly
Through visual
inspection and/or
perform NDT test
7 147 Buy certified components
Misalignment 6
Align components
during
assembly
By double
checking alignment5 150
Follow standardized methods
when aligning parts
Unbalance 6Balance parts
during assambly
By double
checking balance5 150
Follow standardized methods
when balancing parts
Looseness 6Tighten bolts with
appropriate torque
By double
checking torque5 150
Make sure assembly
instructions are followed
Faulty
assembly6
Follow assembly
instructions
By double
checking assembly6 180
Make sure assembly
instructions are followed
Faulty
assembly5
Follow assembly
instructions
By double
checking assembly5 150
Make sure assembly
instructions are followed
Looseness 5Tighten bolts with
appropriate torque
By double
checking torque5 150
Make sure assembly
instructions are followed
Foundation
Support all
components
of the fan
Structural
damage
Increased
vibration6
Geometria
changes7
Impeller
Creates
pressure
differance
between
suction and
discharge side
Structural
damage
Increased
vibration5
Grease
contamination5
Damaged
seals
Support drive
shaft and
impellers
weight.
(radial forces)
and receive
impellers
thrust
(axial force)
Plummer
Block
Bearings
Housing
and/or
fasteners
cracks/breaks
Increased
vibration
9
7
Damaged
adapter sleeve
and lock ring
Increased
temperature7
6Increased
vibration
Damaged Rollers,
inner/outer
raceways
and roller cage
Structural
damage
Failure Modes, Effects, and Causes with Highest RPN for Main Fan 2 in FTP-1
107
As stated in Table 3, in section 5.1.2, the initial plummer block bearings were in operation
from 85k to 136k hours, which is a well acceptable lifetime for bearings that are in
constant operation. It also states that, for main fans 3 and 4, there has only been a single
bearing change on each since they were initially started. For main fan 1, the bearings were
changed in March 2010 which occurred only 16k hours from first change, but after that
they were in operation for approx. 80k hours before they were changed in February 2019.
According to maintenance personnel are the bearings showing some operational problems
and are expected to be replaced soon, as mentioned earlier, indicating there is some factor
causing the short lifetime on the plummer block bearings on main fan 1 as well. The
bearings on main fan 2 have been changed five times since the initial change in the year
2008, which implies that the problem seems to be isolated to the plummer block bearings
on main fans 1 and 2, although the problem seems to be more serious on main fan 2.
From this information it is assumed that some other factor is causing the short bearing life.
By exploring the maintenance history, other components (items) of the main fan setup are
be ruled out as being the cause for the short plummer block bearing life. The electric motor
has never shown any signs of mechanical faults, although it was changed some years back
cause of isolation deterioration after being in operation for approx. 20 years. According to
maintenance personnel has the coupling always appeared to be in good condition and not
shown any signs of mechanical failure, although it is standard procedure to change the
rubbers in the coupling during bearing change. The driveshafts have always appeared in
good condition, but the shafts surface where the axial seals are located have sometimes
shown surface roughness, most likely resulted from grease contamination caused by
bearing deterioration, in these cases the shaft has been replaced along with the bearing
change. According to maintenance personnel, there has only been one incident where
impeller broke during operation, most likely caused by faulty design since there has never
been any other incident like that on any of the other main fans in all the FTP’s. The
foundation supporting the main fans have never shown any signs of problematic operation
and are since the FTP’s were initially started, and since the initial bearings were in
operation as long they were it is almost impossible to link any of the short bearing life to it.
Looking at the plummer block bearing operational parameters listed in Table 10 in
appendix C, the calculated bearing lifetime is over 200k hours for both bearings, however
the relubrication interval is just 20.3 hours for the Fan DE bearing and 2390 hours for the
Fan ND bearing, indicating that the lubricating setup is most likely not ideal for this
bearing setup with the current operational parameters. Another factor that is also noticeable
is that the speed factor (ndm) limit, for the Fan DE bearing is 150,000 with this kind of
lubrication method, but according to the bearing calculations it currently is 209,000 well
above recommended value.
Based on this information it is concluded that the current lubrication method is not ideal
and should be reconsidered. But giving that the initial bearings were in operation as long as
they were, it is most likely some other factor is also contributing to the short bearing life.
In the complete FMEA worksheet, listed in Table 17 in appendix F, one possible cause for
increased vibration from the rollers, inner/outer raceways, and/or roller-cage is listed, that
is cause from magnetic field. Although this particular cause only scored 96 in RPN, it is
hard to ignore it since other possibilities have not been able to pinpoint the problem and the
fact that there is a strong magnetic field in the pot rooms and its surroundings.
108
Possible Effects from Magnetic Field
In the beginning of February 2020 an expert named Marius Friedman, from the company
Flebu International AS, visited Norðuál to check up and give advice for the main fans in
FTP-1, due to the short bearing life. Based on Flebu long experience with fans in
aluminum plants, he stated that they have encountered similar problems before regarding
short bearing life. Based on the location of the main fans between the pot rooms, where the
bearings are exposed to strong magnetic field, there is possibility that the rollers and the
roller-cage are forced together cause of combination from the magnetic field and low load
on the bearings, resulting in unusual wear on the roller-cage.
Based on earlier experience, the expert suggested that the Fan DE bearing should be
changed to a similar bearing with non-magnetic roller-cage and that the grease lubrication
setup would be changed for circulating oil lubrication. Although this seems to be a
probable solution to the problem, it is hard to ignore the fact that the initial bearings were
in operation as long as they were, raising the question what has changed?
According to Norðurál technical department, the company started to gradually increase the
pot rooms amperage in the year 2010 to increase the aluminum production. Where the
strength of the magnetic field generated in the pot rooms is in direct relation to the strength
of the current flowing through it, it is a possibility that the increased magnetic field could
be contributing to the short bearing life. Figure 72 illustrates how the pot room amperage
increase process has developed since January 2010, where initially the designed pot room
amperage was approx. 180 kA in pot rooms A and B (referred to as line 1) and approx. 200
kA in pot rooms C and D (referred to as line 2). The amperage was gradually increased on
both lines until it reached it current value of approx. 220 kA in the end of 2014 on line 2
and in the beginning of 2016 on line 1.
Figure 72: Development of the pot rooms amperage since the year 2010, and time of
bearing change on main fans 1 and 2 in FTP-1.
109
In Figure 72 the yellow and blue vertical lines indicate the dates of plummer block bearing
change on main fan 1 and 2. It shows that the interval on the bearing change has greatly
increased since the amperage reach it current value, especially on main fan 2 although the
bearings on main fan 1 have been having some operational problems recently, according to
maintenance personnel.
Based on this information it is safe to say that there is strong correlation that the increased
pot room amperage is contributing to the short bearing life. But it is hard to assert that the
magnetic field is the main cause for the short bearing life since main fans 3 and 4 in FTP-1
have not been showing the same problem and none of the fans in FTP-2. Unless the
strength of the magnetic field surrounding the housings on the plummer block bearings on
all main fans is measured and compared to each other, to see if there is some difference
between them. Unfortunately, it was not possible to perform these measurements during
this case study.
Unfortunately, the thesis time frame did not allow further failure assessments to be done
and since FMEA was unable to pinpoint the cause, next steps would be to perform Root
Cause Analysis (RCA) to see if the root cause for the short bearing life could be determent.
Consequently, if the RCA would not pinpoint the cause, a Fault Tree Analysis (FTA)
would be the next step. Using infrared thermography was also an option that was
considered but unfortunately the equipment needed to perform those measurements was
not available and would have to be bought especially for this project, with funds that were
not available.
5.3.2 Assessing Condition and Fault Development using
Vibration Analysis
The first step in the vibration measurement part of this thesis was placing the localized
equipment beside main fan 2 in FTP-1. As mentioned earlier, the equipment was set up to
perform a single measurement every 24 hours, with two accelerometers mounted on each
plummer block bearing housing, measuring in axial and radial directions. The equipment
was in operation from April 2019 to May 2020, performing total of 362 measurements
with sampling rate of 2500 Hz and sampling period of 1 second.
The portable equipment arrived later, cause of shipping delays, and was not operable until
the beginning of July 2019, unfortunately it did not arrive in time before the bearing
change on main fan 2 in FTP-1. As mentioned earlier, the same basic function was used for
this equipment where two accelerometers were used on each plummer block bearing to
measure in axial and radial directions with sampling rate of 100 kHz and sampling period
of 1 second.
Initial Condition Assessments
It was decided to perform a single measurement on both plummer block bearings on all
main fans in FTP-1 and FTP-2, to assess their condition and select bearings to monitor.
This initial assessment was performed by analyzing the bearings frequency spectrums to
inspect if any bearing fault frequencies were present or any other indication of faulty state.
For convenience, the bearing fault frequencies are listed again on next page, gathered from
Table 10 in appendix C.
110
Bearing fault frequencies:
• Fan DE: FTF = 7.1 Hz BSF = 57.2 Hz BPFO = 128.4 Hz BPFI = 169.5 Hz
• Fan ND: FTF = 7.0 Hz BSF = 51.9 Hz BPFO = 118.8 Hz BPFI = 162.5 Hz
The assessment results for FTP-1 are listed in Table 7 and for FTP-2 are listed in Table 8.
Table 7: Initial condition assessments on plummer block bearings on main fans in FTP-1.
Table 8: Initial condition assessments on plummer block bearings on main fans in FTP-2.
Main
FanBearing Decision
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Vibration level at shaft frequency is approx. 3 mm/s
Small indication on impellers blade pass frequency.
Indicating unbalance on impeller.
Vibration level at shaft frequency is approx. 1 mm/s
No indication on faults.
Fume Treatment Plant 1: Initial condition assessments, based on vibration measurements
Continue to
Monitor
Continue to
Monitor
(based on history)
Not
Continued
Continue to
Monitor
Vibration level at shaft frequency is approx. 1.7 mm/s
No indication on faults.
Vibration level at shaft frequency is approx. 1.4 mm/s
No indication on faults.
Vibration level at shaft frequency is approx. 1.8 mm/s
No indication on faults.
Vibration level at shaft frequency is approx. 1.2 mm/s
Indication on both BPFI & BPFO fault frequencies and their harmonics.
Indications on vibration rise in the high frequency range, around 5000 Hz.
Vibration level at shaft frequency is approx. 1.7 mm/s
First harmonic of BPFI noticeable.
1
2
3
4
Measurement Remarks
Vibration level at shaft frequency and its second harmonic is approx.
1 - 1.5 mm/s, nothing to rise concerns.
Roller element fault frequency noticeable at its 4th harmonic.
Minor indication on BPFO harmonics.
Main
FanBearing Decision
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fan DE
(Spherical Roller)
Fan ND
(CARB Toroidal Roller)
Fume Treatment Plant 2: Initial condition assessment, based on vibration measurements
Measurement Remarks
2
Vibration level at shaft frequency is approx. 0.7 mm/s
No indication on faults.
Vibration level at shaft frequency is approx. 0.5 mm/s
No indication on faults.
Continued to
Monitor
1
Vibration level at shaft frequency is approx. 0.5 mm/s
No indication on faults. Not
ContinuedVibration level at shaft frequency is approx. 0.7 mm/s
No indication on faults.
Not
Continued
Continued to
Monitor
4
Vibration level at shaft frequency is approx. 2.7 mm/s and increases over
next two harmonics (1st. 2.9 mm/s and 2nd. 3.1 mm/s), indicating possible
looseness and/or misalignmment.
Vibration level at shaft frequency is approx. 1 mm/s
No indication on faults.
3
Vibration level at shaft frequency is approx. 1.7 mm/s
First two harmonics of shaft frequency are showing, indicating possible
misalignment between shaft and motor.
Vibration level at shaft frequency is approx. 0.6 mm/s
No indication on faults.
111
Based on the initial condition assessment it was decided to continue to monitor main fans 1
and 4 in FTP-1 and main fans 3 and 4 in FTP-2. Although plummer block bearings were
changed on main fan 2 in FTP-1 just few days before the portable equipment arrived it was
also included occasionally to see if some faults would develop during the measurement
period. Fan DE bearings on both main fan 1 and 4 in FTP-1, showed indication on bearing
fault frequencies leading to special interest to see how they would develop. Fan DE
bearings on both main fan 3 and 4 in FTP-2 showed indication on possible misalignment
and/or looseness and therefore were included in the following monitoring period to see if
the problem would escalate. They did not show any indication regarding the bearing fault
frequencies and consequently they did not raise any big concerns moving forward.
Continued Measurements and Vibration Analysis
In the period from July 2019 to May 2020 vibration measurements were performed with
the portable equipment, with variable intervals, on the main fans selected from the initial
condition assessments. It appeared that the axial direction sensors on both Fan DE and Fan
ND bearings give more information regarding the frequency spectrum, so in the following
discussion and figures are only the axial sensors used, and each main fan will be discussed
and analyzed separately. The main focus will be on main fans 1 and 4 in FTP-1 since their
initial condition assessment indicated some bearing fault frequencies. The development of
main fan 2, in FTP-1, condition will also be checked, giving its maintenance history is by
far worst. Main fans 3 and 4 in FTP-2 will get some brief discussion on their operational
condition development.
FTP-1: Main Fan 1
There were performed ten measurements on the plummer block bearing housings on main
fan 1, during the case study measurement period. Figure 73 illustrates the 0 to 1300 Hz
frequency spectrum for the Fan DE bearing from July 2019. The vertical dotted lines
indicate the BPFI, BPFO, and BSF fault frequencies and their harmonics. Where the most
vibration is generated at the shaft frequency and its harmonics, as would be expected,
where the 2nd, 6th, and 12th harmonics are visible. This could indicate small misalignment
or looseness, but nothing that raises any concerns. The 4th harmonic of the BSF fault
frequency is showing, with considerably high amplitude (0.4 mm/s) giving it is a bearing
fault frequency, who usually give rather small impulses. There are also small traces of the
BSFO and BSFI fault frequencies harmonics. It indicates that some fault development is
happening inside the bearing, however, the noise floor is rather low in the upper
frequencies which indicates that the faults are not necessarily crucial but important to keep
monitoring.
In Figure 101, in Appendix G, the Fan ND bearing frequency spectrums, from July 2019
and May 2020, are compared, showing that there are only minor indication on BSF fault
frequency and the condition of the bearing has not degraded noticeably and generally
seems to be in good condition. There is some vibration at the shaft speed and on the blade
pass frequency indicating some imbalance on the impeller but has decreased between
measurements therefore not something that rases any concerns. Figure 102, in appendix G,
illustrates the time domain envelopes comparison, for the same measurements, where the
vibration data was band passed filtered with optimal bandwidth gathered from the
kurtogram. Interestingly it shows extremely high kurtosis for both instances, 225 and 587,
indicating high impulsiveness in the signal. This extreme impulsiveness is most likely
112
caused by the fans casing is vibrating close to the Fan ND bearing housing and striking it
randomly.
Figure 73: Fan DE bearing frequency spectrum, on main fan 1 in FTP-1, from July 2019.
Figure 74 illustrates the 0 to 1300 Hz frequency spectrum for the Fan DE bearing since
May 2020. It shows that the vibration at the shaft speed has decreased little bit, but its level
was not of any concerns earlier and remains that way. The amplitude of the BSF 4th
harmonic has increased 42.5% between measurements and is causing concerns. The BPFI
and BPFO have decreased and are not as visible as before, which is not necessarily good
sign since surface spalls often smoothened out with time, meaning that the condition has
possibly gotten worse than before. The increasing noise floor, in the upper frequencies,
also indicates that the bearing faults have increased since before.
Figure 74:Fan DE bearing frequency spectrum, on main fan 1 in FTP-1, from May 2020
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Given the information gathered from the frequency spectrums it was decided to compare
the time domain envelopes and the envelope spectrums between the two measurements.
The comparison between the envelope spectrums did not show any dominant indication
that the bearing condition has gotten worse and did not reveal any strong bearing fault
frequencies or their harmonics. Figure 75 illustrates the comparison between the time
domain envelopes, where the envelopes are calculated similarly as before using optimal
band pass filter to filter the signal. The kurtosis increased little bit between measurements,
meaning increased impulsiveness in the signal, indicating that there have possibly larger
and/or more spalls formed on the rolling elements or inner and/or outer raceways. The
overall vibration level has increased greatly, giving strong indications that the operational
condition has deteriorated since the initial measurement.
Figure 75: Vibration signals envelope comparison between first and last measurements on
Fan DE bearing on main fan 1, in FTP-1.
Figure 76 illustrates the frequency spectrum development in the high frequency range, 1.5
kHz to 25 kHz, for the all the measurements taken on the Fan DE bearing. As described in
section 3.2.2, are increased vibration in the ultrasonic frequency range (often between 20
kHz and 60 kHz, dependent on the bearings structure) the first indication on bearing fault,
and as a fault develops a vibration in the bearing components resonant frequency range
increases (often in the 2 kHz to 10 kHz range). It appears that there is a little bit vibration
increase in the bearing components resonant frequency range, approx. 4 kHz to 9 kHz,
including some development in the 20 kHz range. This gives indication that there are faults
developing in the bearing and it should be monitored more often.
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Figure 76: FTP-1 main fan 1 Fan DE bearing, waterfall graph of the 1.5 kHz to 25 kHz
frequency spectrums, from July 2019 to May 2020.
Figure 77 illustrates a spectrogram comparison between measurements, showing vibration
increase in the high frequency range and it has become more compact, the vibration level
at the lower frequencies has also increased. Giving indication that there are bearing faults
developing in the bearing, giving similar results like the waterfall graph.
Figure 77: Spectrogram of the first and last vibration measurements, on Fan DE bearing.
FTP-1: Main Fan 2
There were performed seven measurements on the plummer block bearing housings, on
main fan 2, with the portable equipment, during the case study measurement period. There
were bearing change performed in July 2019, so the following measurements give initial
condition assessments on the bearing condition and how its condition developed. Figure 78
illustrates the frequency spectrum, 0 to 1300 Hz, for the Fan DE bearing day after it was
installed. Vibration level at shaft speed is rather small, only approx. 1 mm/s, indicating that
the assembly and alignment was performed correctly. The bearing is showing some
vibration at BPFI and BSF fault frequencies indicating possible defects in the bearing, but
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it must be kept in mind that the bearing is new and these impulses are most likely going to
smoothened out in the following days or weeks. The noise floor is also very low, indicating
good condition.
Figure 78:Fan DE bearing frequency spectrum, from July 2019 (Bearing New).
In Figure 103, in Appendix G, the Fan ND bearing frequency spectrums, from July 2019
and May 2020, are compared, showing some indication on BSF fault frequency and its
harmonics in the initial measurement but showing reduction in the latest measurement.
There is a possibility that this is caused by that the bearing is brand new during the first
measurement. There are other indications on faulty state, where the noise floor is low for
both measurements and the condition of the bearing has not degraded noticeably and
generally seems to be in good condition. There is some vibration at the shaft speed
indicating small imbalance on the impeller but has decreased between measurements and
does not raise any concerns. Figure 104, in appendix G, illustrates the time domain
envelopes comparison, for the same measurements, where the vibration data was band
passed filtered before the envelope was calculated, as before. The vibration level is very
low in the first measurement with rather low kurtosis of 13.4, compared to the Fan ND
bearing on main fan 1. In the later measurement the vibration level has increased
considerably, and the kurtosis has increased to 327.5, indicating that the impulsive
components have increase greatly between measurements. As for main fan 1, this extreme
impulsiveness is most likely caused by the fans casing that strikes the bearing housing
randomly.
Because of the fact that the BPFI and BSF fault frequencies were showing in the frequency
spectrum, for the Fan DE bearing, it was decided to analyze the envelope spectrum to see if
anything would reveal itself. Figure 79 illustrates the Fan DE bearing envelope spectrum,
taken in July 2019, where the envelope spectrum is calculated as before. Interestingly the
BPFO fault frequency and its harmonics are showing and the BPFI and BSF fault
frequencies did not appear as they did in the frequency spectrum. However, the amplitude
is rather small and giving it is a new bearing and most likely will reduce in the following
days/weeks and does not rise any major concerns but gives reason to monitor the bearing
regularly in the following weeks/months.
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Figure 79: Envelope spectrum of Fan DE bearing, taken in July 2019 (Bearing New).
Figure 80 illustrates Fan DE bearing frequency spectrum, measured in May 2020, where
the vibration at shaft speed and its 2nd harmonic have decreased between measurements.
The bearing fault frequencies are still visible but have decreased between measurements
which was to be expected. The noise floor has shown minor increase, but the overall
spectrum indicates that the bearing is in good operating condition.
Figure 80: Fan DE bearing frequency spectrum for main fan 2 in FTP-1, from May 2020.
Figure 81 illustrates the envelope spectrum for the measurement performed in May 2020. It
shows that the overall vibration level has increased between measurements, and noticeably
the BPFO fault frequency is no longer as dominant in the spectrum. This gives some
indication that the bearings condition has gotten slightly worse, but nothing that raises any
major concerns.
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Figure 81: Envelope spectrum of Fan DE bearing, taken in May 2020.
Figure 82 illustrates the time domain envelopes displaying similar results as the envelope
spectrum, where both the overall vibration level and the kurtosis, from 3.4 to 8.7, have
increased between measurements indicating more impulsiveness in the signal. However,
the vibration level is still rather low and nothing that should give any concerns.
Figure 82:Vibration signals envelope comparison between first and last measurements
performed on Fan DE bearing on main fan 2, in FTP-1.
Figure 83 illustrates the high frequency range spectrums for all the measurements
performed, with the portable equipment, on the Fan DE bearing. The vibration level first
starts to show an increase in the 10 kHz to 25 kHz range and as time goes the vibration
level decreases in that range and the bearing component resonant frequencies start to show
increasing vibration level. This gives indication on how an early stage bearing fault
progresses from stage one to stage two fault, as discussed in section 3.2.2.
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Figure 83: Fan DE bearing, waterfall graph of the 1 kHz to 30 kHz frequency spectrums,
from July 2019 to May 2020, for main fan 2 on FTP-1.
FTP-1: Main Fan 4
There were performed ten measurements on the plummer block bearing housings on main
fan 4, during the case study measurement period. Figure 84 illustrates Fan DE bearings
frequency spectrums, for main fan 4, from July 2019 and May 2020. The vertical dotted
lines indicate the BPFI and BPFO fault frequencies and their harmonics. The most
vibration is generated at the shaft frequency, as would be expected, but the vibration level
is rather low approx. 1.2 mm/s which indicates that the system is well balanced and
aligned. The BPFO and BPFI fault frequencies are visible in both measurements, giving
strong indication that there are spalls or other damage on the surfaces on the inner and
outer raceways. The BSF fault frequency is not showing any signs, so it appears that the
damages are isolated to the raceways of the bearing. Table 9 shows a comparison on the
amplitude development for BPFI and BPFO fault frequencies and their harmonics for both
measurements. Where the outer raceway fault frequency is showing high increase between
measurements indicating that the potential spalls on the outer raceways surface are
increasing. The inner raceway fault frequency is however showing decrease between
measurements, except for the 3rd harmonic, which is not necessary a good sign since
impact points often get worn out over time and could indicate that the spall has grown
larger between measurements. This bearing should be monitor more frequently in the
upcoming weeks/months and it should be considered for bearing change.
Table 9: BPFO and BPFI amplitudes comparison between measurements
2019-07-05 2020-05-07 Difference 2019-07-05 2020-05-07 Difference1st 0,20 0,32 60% 0,49 0,46 -6%
2nd 0,08 0,15 88% 0,13 0,05 -62%3rd 0,18 0,34 89% 0,42 0,69 64%4th 0,17 0,25 47% 0,44 0,30 -32%5th 0,21 0,22 5% 0,13 0,13 0%6th 0,06 0,06 0% 0,12 0,10 -17%
Ball Pass Frequency Outer (BPFO) Ball Pass Frequency Inner (BPFI)
Har
mo
nic
s
Date
119
Figure 84: Frequency spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-1.
Figure 85: Envelope spectrums comparison between first and last measurements performed
on Fan DE bearing on main fan 4, in FTP-1.Figure 85 illustrates the envelope spectrums
for the first and last measurements performed on Fan DE bearing. The first thing that
strikes the eye is that the shafts frequency in the first measurement is not appearing, which
it usually does. Another thing that is odd, is how the BPFO and BPFI bearing fault
frequencies just appear for their first harmonic, but not for higher harmonics and are not
dominant as they appear in the frequency spectrum. This is confusing and strange, since
envelope spectrum usually enhances the impulsive components in the vibration signal,
which bearing faults certainly are. The noise floor has increased little bit between
measurements but nothing that should raise any concerns since the increase is rather low.
The envelope spectrums unfortunately do not give the information as would be expected,
based on the frequency spectrums clear indication on bearing faults.
Figure 86 illustrates the time domain envelopes for both measurements, where the
vibration signals impulsiveness has increased little bit between measurements when the
kurtosis increased from 3.11 to 3.46. The overall vibration level seems to be similar
between measurements indicating that the bearing condition has not changed that much. A
waterfall graph is illustrated in Figure 105, in Appendix G, where the high frequency
spectrum, 1 kHz to 30 kHz, is plotted for all measurements performed on the Fan DE
bearing during the case study measurement period. Showing no indication on that the
bearing’s operating condition has changed.
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Figure 85: Envelope spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-1.
Figure 86: Vibration signals envelope comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-1.
Figure 106, in Appendix G, illustrates Fan ND bearings frequency spectrums, for main fan
4, from July 2019 and May 2020. Where the BPFO and BPFI fault frequencies are
appearing in both measurements, with some increase in amplitude between measurements.
Indicating that there are spalls or other surface damage present in the inner and outer
raceways of the bearing. The overall vibration level has not shown increase, which is a
good sign and indicates that the bearings condition is acceptable.
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The mounting hole used to fasten the accelerometers on the bearing housing on this
particular bearing, as illustrated in Figure 67 in section 5.2.2, has a broken piece from old
eyebolt in it. There were only approx. 1 to 2 threads usable in the hole and not possible to
fasten the sensors properly, just tighten it loosely and support it with hand during the
measurement. Therefore, the envelope analysis and high frequency spectrum analysis are
unfortunately not usable since the high frequency components of the vibration signal are
most certainly missing or not as they should be.
FTP-2: Main Fan 3
Figure 87 illustrates the comparison between the frequency spectrums, 0 Hz to 1300 Hz,
for the Fan DE, from July 2019 and May 2020. Where the bearing shows no indication on
any bearing fault frequencies for either measurement. Some vibration is visual at the shaft
speed and its harmonics in the first measurement, indicating possible misalignment and/or
looseness, but levels are rather low and do not raise any concerns, and actually has
decreased between measurements. Similar results were obtained for the Fan ND bearing
and is therefore not included in the summary. There was no bearing fault indication drawn
from the envelope analysis either, so the overall condition is considered acceptable for both
Fan DE and Fan ND bearings on main fan 3 in FTP-2 and will not be discussed further.
Figure 87: Frequency spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 3, in FTP-2
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FTP-2: Main Fan 4
Figure 88 illustrates the frequency spectrums, 0 Hz to 1000 Hz, for the first and last
measurements performed on Fan DE bearing on main fan 4 in FTP-2. There is clear sign of
vibration at the shaft speed and its harmonics were noticeably it has increased between
measurements and more harmonics are noticeable. This strongly indicates misalignment
and/or looseness in the system. Since there are so many harmonics visible in the spectrums
and they increase between measurements it is more likely that the problem is an assembly
looseness, as illustrated in Figure 29 in section 3.2.1, which is progressing.
There are no signs or indications on bearing fault frequencies in the frequency spectrums
and the noise floor is rather low in both measurements indicating that the overall bearing
condition is acceptable. However, it is advisable to inspect the bearing and check for the
possibility that there is an assembly looseness between the bearing and the adapter sleeve
or the adapter sleeve and the driveshaft.
Figure 88: Frequency spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-2.
It was decided to perform envelope analysis for both measurements, to see if any bearing
fault would appear. There was no indication on the bearing fault frequencies in the
envelope spectrums. The first measurement however did appear to have strong vibration in
the low frequency range, so it was decided to focus the envelope analysis on the 0 to 200
Hz range. Figure 89 illustrates the envelope spectrums for both measurements where the
FTF fault frequency is showing in the first measurement and the overall vibration level is
rather high. In the last measurement the vibration level has decreased greatly and the FTF
fault frequency is not showing. The excessive vibration and the presence of the FTF in the
first measurement is most likely caused by poor lubrication. Where in the last measurement
there has possibly been grease change shortly before. Unfortunately, the maintenance
history obtained for this case study did not cover these dates.
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Figure 90 illustrates the time domain envelopes for these measurements, where the kurtosis
decreased from 16.5 to 4.7 between measurements. Indicating that the impulsive
components in the signal have decreased. The overall vibration level has also decreased
which supports that the most likely cause is the bearing grease condition between the two
measurements.
Similar process was performed on the Fan ND bearing where the bearing did not show any
signs of bearing faults. It showed small indication of misalignment and/or looseness but
nothing that raises any concerns. The overall condition is acceptable and will not be
discussed further.
Figure 89: Envelope spectrums comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-2.
Figure 90: Vibration signals envelope comparison between first and last measurements
performed on Fan DE bearing on main fan 4, in FTP-2.
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5.3.3 Remaining Useful Life Estimation
Decision was made to use the vibration measurements gathered from Fan DE bearing on
main fan 2 in FTP-1 using the localized equipment. Based on the fact that this is the only
data that spans over the last few months that the prior bearings were in operation, along
with the new bearings and their operation since July 2019. The measurements collected
with the portable equipment are unfortunately too few to make any sensible estimation.
It is not always clear on what type of fitting curve should be used for the RUL estimation
but, using a linear curve is widely used method which assumes that the bearing faults
severity is a linear process. Exponential fitting curves are also widely used, especially in
cases where it is known that the fault can have feedback effects which increase the
deterioration rate exponentially. It is possible to fit other types, but it not widely used
unless in cases where it is known to fit the data, based on experience or other physical
reasons [1]. It was decided to use both linear and exponential curves to estimate the RUL
on the Fan DE bearing to compare their results.
The overall RMS vibration level is a well-known condition indicator for rotating
machinery, so it was decided to perform the estimation of the RUL using the RMS-values.
Figure 91 illustrates the overall RMS vibration level for every measurement performed on
the Fan DE bearing on main fan 2 in FTP-1, excluding the measurements when the fan was
shut off because of maintenance work. It is visible how the vibration level dropped down
after the bearing change in July 2019, but it is noticeable that the trend is slowly going
upwards as one would expect.
Figure 91: Overall RMS vibration level for all measurements performed on the Fan DE
bearing, on main fan 2 in FTP-1.
First step in performing a bearing remaining useful life estimation is to establish some kind
of threshold which indicates that the bearing is at its end of life. The overall RMS value
does fluctuates quite bit between measurements, as seen in Figure 91, hence the method
125
used to establish the threshold was calculating the mean RMS value over the last 15 days
before the bearing change occurred, resulting with the threshold,
RMS-Threshold = 2.75 m/sec2
Next step was to trend the RMS values for all measurements performed after the bearing
change in July 2019 with both linear and exponential curves. The curves coefficients were
found using the fit-function in MATLAB®, resulting with the following fitting equations,
𝐿𝑖𝑛𝑒𝑎𝑟 𝑇𝑟𝑒𝑛𝑑𝑖𝑛𝑔: 𝐹𝑖𝑡𝐿𝑖𝑛(𝑥) = 0.002243𝑥 + 1.388 (63)
𝐸𝑥𝑝𝑜𝑛𝑒𝑡𝑖𝑎𝑙 𝑇𝑟𝑒𝑛𝑑𝑖𝑛𝑔: 𝐹𝑖𝑡𝐸𝑥𝑝(𝑥) = 1.403 ∗ 𝑒0.001341𝑥 (64)
Figure 92 illustrates the overall RMS vibration level for all measurements performed after
the bearing change, in July 2019, including the linear and exponential trending curves. The
horizontal red line is the RMS-threshold established earlier. The RUL estimation is
established where the trending curves cross the RMS-threshold. For the linear curve, the
estimated RUL is 333 days and for the exponential curve it is 228 days. Based on the
exponential estimation the maintenance personnel should prepare for bearing change in
December this year but based on the linear estimation they should prepare in April 2021.
Figure 92: Overall RMS vibration level for all measurements after bearing change, with
linear and exponetial trending curves.
Other condition indicators were also checked but unfortunately their parameters were not
easy to trend to make some sensible estimation on the RUL of the bearing. Figure 93
illustrates the trends for Mean Frequency, Skewness, Kurtosis, and Crest-Factor for all the
measurements performed on the Fan DE bearing on main fan 2 in FTP-1. These condition
indicators are not showing any easy trend able options to perform RUL estimation. The
reason for this is most likely because the localized measurement equipment has too low
sampling frequency, only 2500 Hz. Meaning it does not pick up the bearing components
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resonant frequencies, approx. 3 kHz to 10 kHz, which is the frequency range that bearing
faults usually start to appear, excluding the supersonic frequency range. Giving that the
localized equipment sample rate was 2500 Hz using a 1000 Hz low pass filter, means that
bearing faults that are measurable with this equipment have usually progressed to stage 3,
where the bearing fault frequencies are starting to appear in the frequency spectrum.
To be able to explore these condition indicators, as illustrated in Figure 93, further and
perform RUL estimation based on them, the sampling frequency most likely should have
been at least 25 kHz using a 10 kHz low pass filter. Unfortunately, that kind of equipment
is much more expensive than the one used and was outside the project budget.
Figure 93: Mean frequency, Skewness, Kurtosis, and Crest factor for all measurements
performed on the Fan DE bearing, on main fan 2 in FTP-1.
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6 Conclusions
The main focus of this project was to design and develop a vibration measurement and
analysis system that is used to collect vibration data and utilize that data to perform
advanced vibration analysis. The system was utilized in a case study at Norðurál aluminum
plant. The purpose for the case study was because of problems Norðurál had been having
regarding one of the centrifugal main fans in one of the companies fume treatment plants
(FTP). The problem described in a way that one of the main fans plummer block bearings
was experiencing to short lifetime, resulting in unexpected shutdowns and interruptions in
the fan’s operation.
Failure Mode and Effect Analysis (FMEA) was performed on the main fan 2, in FTP-1,
setup to try to narrow down and isolate the cause for the unusual short plummer block
bearing lifetime. The analysis showed that the current lubrication setup for the plummer
block bearings was not suitable based on the systems operating condition. However, the
analysis was not able to pinpoint the problems cause, based on maintenance history and
prior lifetime of the bearings. Although possible effects from magnetic field did come up
in the analysis, it was not able to strongly indicate that was the cause. The fact that an
expert, that was brought in by the company to assess the main fans setup, did point out
based on prior experience that a spherical roller bearings operating in a strong magnetic
field are affected by it, resulting in an unusual short lifetime. Another factor that supports
this theory is the fact that the company did increase the pot rooms amperage in the recent
years. Based on this information it is likely that a combined effect from the magnetic field
and the unsuitable lubrication method are the cause for the unusual short plummer block
bearing life.
Two sets of vibration equipment’s were used within the case study, where one was
commercially bought (portable equipment) and the other was partially constructed from
scratch and partially bought from known vendors (localized equipment). The localized
equipment was placed beside main fan 2 in FTP-1, to gather vibration measurements once
a day for over a year. The portable equipment was used to perform occasional
measurements with irregular intervals, with much higher sampling rate and resolution. The
vibration analysis was divided into condition assessments and remaining useful life (RUL)
estimation. The condition assessments were made on all the main fans plummer block
bearings in both FTP-1 and 2, using the vibration data gathered with the portable
measurement equipment. The RUL estimation was conducted using the vibration data
gathered with the localized measurement equipment. In both cases the vibration analysis
software, developed for this thesis, was used to perform all the vibration analysis needed to
conduct the condition assessments and RUL estimation.
The results from the condition assessments showed that the current condition on the main
fans plummer block bearings, in FTP-1 and 2, varied a lot between bearings and fans.
Where some bearings showed no fault indication, when others showed one or more
indications on faulty states e.g. bearing fault frequencies, unbalance, looseness, and/or
misalignment. The overall condition assessment showed that the Fan DE bearings
(spherical roller bearings) are in worse condition than the Fan ND bearings (CARB
128
toroidal roller bearings). Which harmonics with the results drawn from the FMEA, that the
lubrication method used for the Fan DE bearings is not suitable.
For the RUL estimation fortunately, there was bearing change performed approx. two
months after the localized equipment was put in operation. Meaning that the equipment
had gathered vibration data prior and after the bearing change. Unfortunately, the low
sampling rate of the localized equipment limited the options for constructing a condition
indicator. The solution was to use the overall RMS vibration level as condition indicator,
where the threshold value was decided to be the mean RMS-value for the last 15 days
before the bearing change. Linear and exponential trending curves were used to trend the
data gathered after the bearing change to establish the RUL estimation.
Future work would be to perform further fault assessments based on the Root Cause
Analysis and the Fault Tree Analysis, to get deeper understanding on the problem and to
find the root cause. Measuring the strength of the magnetic field surrounding the plummer
block bearings are also an interesting follow up to compare the results between bearings to
see if there is any difference in the strength of the magnetic field surrounding them, given
the fact that their maintenance history is by far worst for main fans 1 and 2 in FTP-1.
Upgrading the localized equipment is advised if it is to be used for further condition
monitoring.
According to the technical department at Norðurál, the company has ordered new plummer
block bearings to replace the ones on main fans 1 and 2 in FTP-1. The new bearings have
the same features, excluding the roller cage which is made out of nonmagnetic material
(copper). They also decided to change the method used to lubricate the bearings, from
grease to circulating oil lubrication. These changes will be made next fall or winter and it
would be interesting to monitor their performance and see if the operational condition
improves.
129
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introduction-to-filters/. [Accessed 15 February 2020].
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2002.
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131
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133
Appendices
Appendix A: ISO 10816
ISO 10816-1 is the basic document describing the general requirements for evaluating the
vibration of various machine types when the vibration measurements are made on non-
rotating parts. ISO 10816-3 provides specific guidelines for assessing the severity of
vibration measured on bearings, bearing pedestals, or housings of industrial machines
when measurements are made in situ. It suits machine sets with power above 15 kW and
operating speeds between 120 r/min and 15.000 r/min, including generators, electrical
motors, blowers, and fans [32]. Figure 94 illustrates the standards vibration severity
classification.
The standard defines four groups of machines, based on size, base, and purpose and
categorizes the working condition into four zones:
• Zone A (green): Vibration level good, e.g. vibration from new machine.
• Zone B (yellow): Vibration level satisfactory, continuous operation without any
restrictions.
• Zone C (orange): Vibration level unsatisfactory, condition satisfactory for a
limited period of time.
• Zone D (red): Vibration level unacceptable, condition unacceptable and damage
could occur at any time.
Figure 94: ISO 10816-3 vibration severity classification.
Rigid Flexible Rigid Flexible Rigid Flexible Rigid Flexible
Zone A
Zone B
Zone C
Zone D
Vibration Severity per ISO 10816-1
0
0 - 1,4
R.M.S. Vibration
Velocity: mm/s
1,4 -2,3
2,3 - 2,8
2,8 - 3,5
3,5 -4,5
4,5 - 7,1
7,1 - 11,0
Machine Type
> 11,0
Group 1 Group 2 Group 3 Group 4
Medium-size machines
with rated power above
15 kW and up to and
including 300 kW;
electrical machines
with shaft height
160 mm ≤ H < 315 mm
Pumps with multivane
impeller and with
separate driver
(centrifugal, mixed
flow,
or axial flow)
with rated power
above 15 kW
Pumps with multivane
impeller and with
integrated driver
(centrifugal, mixed
flow,
or axial flow)
with rated power
above 15 kW
Large machines with
rated power above
300 kW and not more
than 50 MW;
electrical machines
with shaft height
H ≥ 315 mm
134
Appendix B: Vibration Severity Chart
General vibration severity chart, from John S. Mitchell’s 1981 Introduction to Machinery
Analysis and Monitoring, is illustrated in Figure 95.
Figure 95: General vibration severity chart, for rotating machinery.
135
Appendix C: Case Study’s Bearing-, Lubricant-, and Maintenance Data
Information listed in Table 10 were gathered using SKF® Bearing Select v1.2-36 software,
which can be downloaded from the SKF website www.SKF.com. Where the input data was
based on forces and dimensions illustrated in Figure 52, the grease currently being used,
normal cleanliness, and operating temperature approx. 70°C.
Table 10: Information on main fan plummer block bearings data, operational data, and
bearing fault frequencies.
Type: Spherical Roller CARB Toroidal Roller
Designation: 23230 CCK/W33 C 2230 K
Housing: SNL 530 SNL 530
Adapter Sleeve / Lock Nut / Locking Device H 2330 / KM 30 / MB 30 H 3130 L / KML 30 / MBL 30
Oil Seal: TSN 530 U 2TSN 530 U
Locating Rings 2FRB 5/270 2FRB 16.5/270
Mounting Method: SKF Drive-Up SKF Drive-Up
Bore (d): [mm] 150 150
Outer Diameter (D): [mm] 270 270
Width (B): [mm] 96 73
Pitch Diameter (P): [mm] 211.3 206.5
Contact Angle (O): [deg°] 13.167 0
Number of Rollers (N): 18 17
Dynamic Load Rating (C): [kN] 1129 980
Static Load Rating (Co): [kN] 1460 1220
Fatigue Load Limit (Pu): [kN] 137 114
Limiting Speed [r/min] 2200 3200
Bearing Weight [kg] 23.0 17.5
Lubrication Type (grease): SKF LGHP2 SKF LGHP2
Radial Load (Fr): [kN] 3.9 18.9
Axial Load (Fa): [kN] 17.0 0
Minumum Load Met: Yes Yes
Rotational Speed: [r/min] 993 993
Rotating Ring: Inner Inner
Shaft Orientation: Horizontal Horizontal
Operational Temperature (Approx.): [°C] 60 60
Relubrication Interval: [h] 20.3 2390
Speed Factor (Limit): [mm/min] 209000 (150000) 205000 (350000)
Bearing Rating Life: [h] > 200000 * > 200000 *
Fans Power Consumption (each) [kW]
Shaft: [Hz] 16.55 16.55
Cage (FTF): [Hz] 7.1 7.0
Inner Raceway (BPFI): [Hz] 169.5 162.5
Outer Raceway (BPFO): [Hz] 128.4 118.8
Rolling Element (BSF): [Hz] 57.2 51.9
approx. 750 kW
* For rating life results above 100000 hours, other failure modes will dominate and limit the life of the bearing.
Bearing Data
Operational Data
Bearing Fault Frequencies
136
Table 11: Fan DE and ND Bearings Lubricant Technical Data (optained from SKF
website).
Designation SKF LGHP 2
DIN 51825 code K2N-40
NLGI consistency class 2–3
Thickener Di–urea
Colour Blue
Base oil type Mineral
Operating temperature range –40 to +150 °C
Dropping point DIN ISO 2176 >240 °C (>465 °F)
cSt at 40 °C 96
cSt at 100 °C 10,5
60 strokes, 1/10 mm 245–275
100 000 strokes, 1/10 mm 365 max.
Mechanical stability Roll stability, 50 hrs at 80 °C, 1/10 mm 365 max.
Corrosion protection Emcor: – standard ISO 11007 0–0
Water resistance DIN 51 807/1, 3 hrs at 90 °C 1 max.
Oil separation DIN 51 817, 7 days at 40 °C, static, % 1–5
Lubrication ability R2F, running test B at 120 °C Pass
Copper corrosion DIN 51 811 1 max. at 150 °C (300 °F)
Rolling bearing grease life R0F test L50 life at 10 000 r/min., hrs 1 000 min. at 150 °C (300 °F)
Fretting corrosion ASTM D4170 (mg) 7*
Shelf life 5 years
Base oil viscosity
Penetration DIN ISO 2137
* Typical value
137
Table 12: Maintenance history for fume treatment plants 1 and 2, for bearings and grease
change interval, for the years 2008 to the summer of 2019.
Note: Since the end of 2018, the documentation on grease change has been registered
differently in the maintenance software, but according to maintenance personnel they are
still performing grease change, along with weekly grease injection on selected bearings.
Main Fan
Cost Date
Running
hours
(current)
Cost Date
Running
hours
(current)
Cost Date
Running
hours
(current)
Cost Date
Running
hours
(current)
46.497$ 2008-05-10 87.144 53.136$ 2008-03-18 85.872 25.838$ 2012-09-24 125.496 25.391$ 2013-11-25 135.744
19.507$ 2010-03-12 16.104 20.994$ 2013-04-02 44.184 (66.768) (56.520)
20.000$ 2019-02-18 78.360 29.926$ 2017-09-25 39.288
(10.656) 9.121$ 2017-12-11 1.848
21.964$ 2018-02-17 1.632
20.000$ 2019-07-05 12.072
(7.368)
704$ 2010-10-26 679$ 2010-10-29 578$ 2010-11-01 451$ 2010-11-19
239$ 2011-04-14 4.080 346$ 2011-04-20 4.152 633$ 2011-04-27 4.248 380$ 2011-05-03 3.960
272$ 2011-10-25 4.656 349$ 2011-10-18 4.344 516$ 2011-10-26 4.368 447$ 2011-11-03 4.416
308$ 2012-04-12 4.080 465$ 2012-04-17 4.368 238$ 2012-05-29 5.184 466$ 2012-04-27 4.224
545$ 2012-10-02 4.152 558$ 2012-10-11 4.248 1.080$ 2012-10-03 3.048 380$ 2012-10-23 4.296
235$ 2013-01-21 2.664 462$ 2013-10-18 8.928 534$ 2012-10-17 336 546$ 2013-04-26 4.440
362$ 2013-02-06 384 653$ 2013-12-27 1.680 473$ 2013-04-15 4.320 472$ 2013-10-21 4.272
424$ 2013-09-30 5.664 1.586$ 2014-04-23 2.808 636$ 2013-12-09 5.712 340$ 2014-05-05 4.704
415$ 2013-10-29 696 668$ 2014-06-18 1.344 627$ 2014-05-08 3.600 508$ 2014-09-11 3.096
511$ 2014-04-01 3.696 445$ 2014-09-01 1.800 354$ 2014-05-15 168 390$ 2015-03-09 4.296
427$ 2014-09-02 3.696 259$ 2015-03-02 4.368 679$ 2014-09-08 2.784 394$ 2015-09-03 4.272
546$ 2015-02-26 4.248 627$ 2015-09-01 4.392 834$ 2015-03-24 4.728 220$ 2016-02-24 4.176
506$ 2016-02-18 8.568 317$ 2016-02-22 4.176 405$ 2015-09-03 3.912 559$ 2016-10-18 5.688
234$ 2016-08-17 4.344 504$ 2016-10-11 5.568 383$ 2016-02-23 4.152 409$ 2017-02-24 3.096
696$ 2017-02-20 4.488 256$ 2017-02-21 3.192 685$ 2016-10-18 5.712 470$ 2017-08-22 4.296
367$ 2017-08-03 3.936 631$ 2017-06-23 2.928 510$ 2017-02-22 3.048 595$ 2018-04-05 5.424
585$ 2018-04-03 5.832 634$ 2017-11-14 3.456 976$ 2017-11-07 6.192 969$ 2018-10-19 4.728
596$ 2018-04-06 3.432 750$ 2018-04-04 3.552
226$ 2018-09-27 4.176 806$ 2018-10-18 4.728
Total Cost 93.381$ 165.403$ 37.534$ 33.387$
Main Fan
Cost Date
Running
hours
(current)
Cost Date
Running
hours
(current)
Cost Date
Running
hours
(current)
Cost Date
Running
hours
(current)
33.953$ 2015-04-27 86.808
(44.088)
624$ 2008-05-07 425$ 2008-05-07 534$ 2008-05-07 684$ 2008-05-07
497$ 2010-11-08 21.960 335$ 2010-11-23 22.320 560$ 2010-11-29 22.464 584$ 2010-12-09 22.704
318$ 2011-05-10 4.392 411$ 2011-05-17 4.200 303$ 2011-06-01 4.416 378$ 2011-05-31 4.152
472$ 2011-11-08 4.368 321$ 2011-11-28 4.680 543$ 2011-12-12 4.656 295$ 2011-11-28 4.344
245$ 2012-05-04 4.272 313$ 2012-05-11 3.960 287$ 2012-05-18 3.792 517$ 2012-06-07 4.608
241$ 2012-10-30 4.296 554$ 2012-11-06 4.296 731$ 2012-11-14 4.320 412$ 2012-11-21 4.008
601$ 2013-01-08 1.680 361$ 2013-05-24 4.776 395$ 2013-05-24 4.584 627$ 2013-05-24 4.416
425$ 2013-04-30 2.688 596$ 2013-12-09 4.776 415$ 2013-12-02 4.608 616$ 2013-12-27 5.208
797$ 2013-11-29 5.112 619$ 2014-04-25 3.288 411$ 2014-04-29 3.552 429$ 2014-05-02 3.024
547$ 2014-04-22 3.456 651$ 2014-09-18 3.504 544$ 2014-09-22 3.504 777$ 2014-09-25 3.504
436$ 2014-09-15 3.504 477$ 2015-03-16 4.296 667$ 2015-03-19 4.272 521$ 2015-03-23 4.296
114$ 2015-03-12 4.272 545$ 2015-09-10 4.272 487$ 2015-09-15 4.320 478$ 2015-09-17 4.272
581$ 2015-09-08 4.320 682$ 2016-03-09 4.344 384$ 2016-03-14 4.344 382$ 2016-03-16 4.344
472$ 2016-03-07 4.344 361$ 2016-11-01 5.688 557$ 2016-11-03 5.616 790$ 2016-11-03 5.568
364$ 2016-10-31 5.712 590$ 2017-03-01 2.880 656$ 2017-03-02 2.856 667$ 2017-03-08 3.000
656$ 2017-02-28 2.880 511$ 2017-10-24 5.688 427$ 2017-10-25 5.688 454$ 2017-10-26 5.568
454$ 2017-10-23 5.688 771$ 2018-03-19 3.504 765$ 2018-03-19 3.480 930$ 2018-03-19 3.456
568$ 2018-03-19 3.528 694$ 2018-10-24 5.256 585$ 2018-10-25 5.280 614$ 2018-10-26 5.304
693$ 2018-10-23 5.232
Total Cost 46.137$ 9.216$ 9.251$ 10.155$
1 2 3 4
Grease
Change
Bearing
Change
Fume Treatment Plant 2
Fume Treatment Plant 11 2 3 4
Bearing
Change
Grease
Change
138
Appendix D: Case Study’s Localized- and Portable Equipment
Table 13: Components list for the localized equipment low-pass filter unit
(6 filters in one box).
Size Qty.
Capacitors 33 nF 18
47 nF 6
100 nF 6
150 nF 12
470 nF 6
22 μF 6
Resistors 1 kΩ 36
22 kΩ 6
43 kΩ 6
470 kΩ 6
LED-diode (Red) 1
Constant-Current diode (approx. 4.8 mA) 1
Operational Amplifiers (TI LM324N) 6
BNC-Connectors 6
Power Supply (12 V Battery) 2
Battery Charger (230V/24V) 1
DC Connections for Battery Charger (Male and Female) 1
Box 1
On/Off Switch 1
Electrical Boards 2
139
Table 14: NI-USB-6000 Analog-to-Digital converter specifications
For more detailed specifications visit: https://www.ni.com/pdf/manuals/374113c.pdf
Analog Inputs
Number of Analog Inputs 8, single-ended
Input Resolution 12 bits
Maximum Sample Rate (aggregate) 10 kS/s
Converter Type Successive approximation
AI FIFO 2047 samples
Timing Resolution 125 ns (8 MHz timebase)
Timing Accuracy 100 ppm of actual sample rate4
Input Range ±10 V
Working Voltage ±10 V
Input Impendance >1 MΩ
Overvoltage Protection ±30 V
Trigger Sources Software, PFI 1
System Noise 10 mVrms
Absolute Accuracy at full scale, single-ended 26 mV (135mV at max over temp.)
Digital I/O
Number of Digital I/O 4
Function
P0.0/PFI 0 Static Digital I/O or counter source
P0.1/PFI 1 Static Digital I/O or AI Start Trigger
P0.2 Static Digital I/O
P0.3 Static Digital I/O
Direction Control Each channel individually programmable as
input or output
Output Driver Type Each channel individually programmable as
open collector or active drive
Absolute Maximum Voltage Range 0 V to 5 V with respect to D GND
Pull-Down Resistor 47.5 kΩ to D GND
Power-On State Input
Counter
Number of Counters 1
Resolution 32 bits
Counter Measurements Edge counting, rising or falling
Counter Direction Count up
Counter Source PFI 0
Maximum Input Frequency 5 MHz
Minimum High Pulse Width 100 ns
Minimum Low Pulse Width 100 ns
NI-USB-6000 Specifications
140
Table 15: Vibration transducers specifications for the localized equipment.
Sensitivity ( ±10%) 10.2 mV/(m/s^2)
Measurement Range ±500 m/s^2
Frequency Range 0.32 Hz to 10000 Hz
Mounted Resonant Frequency 25 kHz
Amplitude Linearity ≤1%
Transverse Sensitivity ≤5%
Shock Limit 70,000 m/s^2 pk
Temperature Range -54°C to 85°C
Waterproof Design
Settling Time 2.5 sec
Excitation Voltage 18 VDC to 28 VDC
Excitation Constant Current 2 mA to 20 mA
Output Impedance <100 Ω
Output Bias Voltage 8 VDC to 12 VDC
Electrical Case Isolation >10,000,000 Ω
Electrical Protection RFI/ESD
Integral Cable 22 AWG, 105°C
Size 9.14 x 9.55 mm
Weight (with 5m cable) 70.7 g
Mounting Thread 1/4-28 UNF-2B
Mounting Torque 2.7 N-m to 6.8 N-m
Sensing Element Ceramic/Shear
Case Material Stainless Steel
Sealing Potted
Wrench Flats 7/16''
1 Hz 850 (µm/s²)/Hz^0.5
10 Hz 100 (µm/s²)/Hz^0.5
100 Hz 27 (µm/s²)/Hz^0.5
1000 Hz 10 (µm/s²)/Hz^0.5
CMCP1100 Standard SpecificationsDynamic Performance
Enviromental
Electrical
Mechanical
Spectral Noise
141
Figure 96: DT9837B Dynamic Signal Analyzer's block diagram.
Note: For more detail discussion and explanation visit:
https://www.mccdaq.com/PDFs/manuals/UM9837.pdf
142
Table 16: Wilcoxon model 736T high frequency accelerometer specifications.
Sensitivity (±5%, 25°C) 100mV/g
Measurement Range 50 g peak
Amplitude Nonlinearity 1%
Frequency Response:
±5% 5.0 - 15,000 Hz
±3 dB 2.0 - 25,000 Hz
Resonance Frequency 60,000 Hz
Transverse Sensitivity, max 7% of axial
Temperature Response:
-50°C -10%
+120°C 5%
Shock Limit 5,000 g peak
Temperature Range -50°C to 120°C
Vibration Limit 500 g
Electromagneticsensitivity, equiv. g 100 µg/gauss
Base Strain Sensitivity 0.005 g/µstrain
Power Requirement: Voltage / Current 18 - 30 VDC / 2 - 10 mA
Electrical Noise, equiv. g:
Broadband 2.5 Hz to 25,000 Hz 150 µg
Spectral 10 Hz 10 µg/√Hz
100 Hz 2 µg/√Hz
1,000 Hz 1 µg/√Hz
10,000 Hz 0.8 µg/√Hz
Output Impedance, max 150 Ω
Output Bias Voltage 10 VDC
Grounding Case grounded
Sensing Element PZT ceramic / compression
Weight 13 g
Material 316L Stainless Steel
Mounting 10-32 tapped hole
Output Connector 10-32 coaxial
Mating Connector R1
Recommended Cabling J93
Wilcoxon Accelerometer: Model 736T Specifications
Electrical
Dynamic Performance
Enviromental
Mechanical
143
Appendix E: Case Study’s Vibration Measurement and Analysis Software
Figure 97: Visualization on GUI operation, Select data type, load data, and select
amplitude representation.
144
Figure 98: Visualization on GUI operation, Selecting sensor and weighting window.
145
Figure 99: Visualization on GUI operation. Bearing fault frequencies loaded from
database and manually calculated, scale settings, and display of bearing fault frequencies
and geometric parameters.
146
Figure 100: Visualization on GUI operation. Measurement settings, CI selection,
Spectrogram setting, and Envelope settings.
147
Appendix F: Case Study’s, FTP-1 Main Fan 2 FMEA Worksheet
Table 17: FMEA-worksheet for main fan 2 in FTP-1 (spreads over three pages).
Excessive load 4Monitor motor
load
Monitor motor
voltage and current2 56
Continue to monitor
motor load
High ambient
temperature6
Make sure ventilation
is satisfying
Monitor ambient
temperature2 84
Set up automatic temperature
measurements with warning
system
To high or low
voltage/current
supply
4
Equip motor with
current and voltage
pretection
Monitor motor
voltage and current2 56
Set up automatic voltage/current
measurements with warning
system
Lack of ventilation 5Make sure ventilation
is satisfying
Increased ambient
temperature2 70
Make sure motor gets
appropriate ventilation
Broken motor fan 4Inspect motor fan
regularly
Motor temperature
increases3 84
Set up regular
inspections schedule
Isolation degrades 3
Prevent over-heating,
minmize corrosion,
and
physical wear/damage
Through visual
inspection8 168
Set up regular
inspections schedule
Safety brakers fail 3 Test brakers regularlyThrough regular
inspection4 120
Set up regular
inspections schedule
Bearing friction 2Keep bearings greased
and cool
Monitor bearings
temperature4 80
Automatically monitor
bearings temperature
Contamination e.g.
oil or other
flamable leftovers
catch fire
2Clean regularly all
contamination
Through visual
inspection7 140
Establish
regular cleaning
schedule
Neighboring work
e.g. Welding/blowtorch2
Make sure all work is
performed with safety
Through visual
inspection7 140
Perform welding and blowtorch
work in teams were one team
member performs visual
inspection
Increased bearing wear 4 3 48
High operating
temperature4 3 48
Motor components
can get damaged 2 3 24
Set up regular
inspections schedule
Material fatigue 2Examine parts
before assembly
Through visual
inspection and/or
Perform NDT test
7 140 Buy certified components
Excessive load 4 Monitor the loadWith automatic
measurements2 80
Continue to monitor
motor load
Faulty assembly 4Follow assembly
instructions
By double
checking assembly5 200
Make sure assembly
instructions are followed
Increased
Vibration4
Unbalance and/or
misalignment5
Align and balance
when assembling
By double
checking assembly5 100
Follow standardized methods
when balancing/aligning parts
Faulty assembly 5Follow assembly
instructions
By double
checking assembly5 100
Make sure assembly
instructions are followed
Misalignment 5Align parts
during assembly
By double
checking assembly5 100
Use certified equipment to
perform alignment/balancing
Unbalance 6Balance parts
during assambly
By double
checking balance5 120
Follow standardized methods
when balancing parts
Looseness 4Tighten bolts with
appropriate torque
By double
checking torque5 80
Use certified equipment
to apply torque
Misalignment 5Align parts
during assembly
By double
checking alignment5 100
Use certified equipment
to perform alignment
Misalignment 5Align parts
during assembly
By double
checking alignment5 75
Use certified equipment
to perform alignment
Excessive load 4 Monitor loadWith automatic
measurements2 24
Continue to monitor
motor load
Material fatigue 2Examine parts
before assembly
Through visual
inspection and/or
Perform NDT test
7 140 Buy certified components
Excessive load 3 Monitor loadWith automatic
measurements2 60
Continue to monitor
motor load
Excessive load 3 Monitor loadWith automatic
measurements2 42
Continue to monitor
motor load
Faulty assembly 5Follow assembly
instructions
By double
checking assembly5 175
Make sure assembly
instructions are followed
Construction error 3 Use certified partsThrough visual
inspection5 105 Buy certified components
Rubbing 4Monitor
temperature
With automatic
measurements3 84
Set up automatic temperature
monitoring
Excessive load 3 Monitor loadWith automatic
measurements2 42
Continue to monitor
motor load
Faulty design 3 Use certified partsThrough visual
inspection5 105 Buy certified components
Drive shaft
breaks10
Permanent
Bent7
Drive shaft
twists7
Drive Shaft
Deliver
torque
to impeller
Structural
damage
FMEA worksheet for Main Fan 2 in FTP-1
Ite
m
Function
Potential
Failure
Mode
Potential
Effect(s)
of Failure Seve
rity Potential
Cause(s)
of Failure
Occ
urr
en
ce
Current Design
Controls
(Prevention)
Current Design
Controls
(Detection)
Monitor bearings vibrations
regularly
De
tect
ion
R P
N Recommended
Action(s)
Electric
Motor
Prime
mover
Electrical
overload
Over-Heating 7
Motor catches
on fire10
Excessive
vibration
Decreased
bearing life4
Monitor bearings
temperature and
vibration
Through automatic
and/or regular
inspections and
measurement
Coupling
Connect
motor
and
drive shaft
Structural
and/or
fasteners
damage
Coupling
shatters10
Damaged
rubber
coupling
Increased
wear4
Increased
Vibration4
Increased
temperature3
148
Continued from Table 17
Material friction 4 Monitor temperatureWith automatic
measurements3 84
Continue to monitor
temperature
Unsufficient
lubrication5
Monitor grease
condition
With regular
checks4 140
Set up regular
inspections schedule
Excessive load 3 Monitor loadWith automatic
measurements3 63 Continue to monitor load
Faulty assembly 5Follow assembly
instructions
By double
checking assembly6 210
Make sure assembly
instructions are followed
High ambient
temperature5
Make sure ventilation
is satisfying
Monitor ambient
temperature3 105
Insure ventilation and make
arrangements if necessary
Reduced
grease life5
Monitor grease
condition
With regular
checks6 210
Set up regular
inspections schedule
Increased
clearance5 Monitor vibration
With automatic
measurements6 180
Monitor bearings vibrations
regularly
Faulty assembly 5Follow assembly
instructions
By double
checking assembly6 180
Make sure assembly
instructions are followed
Spalls on surface 6 Monitor vibrationWith automatic
measurements6 216
Monitor bearings vibrations
regularly
Faulty design 2 Use certified partsThrough visual
inspection6 72 Buy certified components
Excessive load 3 Monitor loadWith automatic
measurements3 54 Continue to monitor load
Reduced grease life 5Monitor grease
condition
With regular
checks6 180
Set up regular
inspections schedule
roller cage breaks 3Monitor vibration and
use certified parts
With automatic
measurements5 90
Monitor bearings vibrations
regularly and use certified parts
Unbalance 4Balance system
during assembly
By double
checking balance5 120
Follow standardized methods
when balancing parts
Misalignment 4Align components
during assembly
By double
checking alignment5 120
Follow standardized methods
when aligning parts
Magnetic field 2
Monitor magnetic field
and possibly use some
protection shield
By measurements 8 96Use nonmagnetic parts
when possible
Faulty assembly 5Follow assembly
instructions
By double
checking assembly6 270
Make sure assembly
instructions are followed
Material fatigue 2Examine parts
before assembly
Through visual
inspection and/or
perform NDT test
7 126 Buy certified components
Excessive load 3 Monitor loadWith automatic
measurements3 81 Continue to monitor load
Foreign object
impacts4
Use protective
covers
Through visiual
inspection5 180
Make sure foreign objects
cannot impact components,
set up protective covers
Faulty design 2 Use certified partsThrough visiual
inspection5 90 Buy certified components
Looseness 6Tighten bolts with
appropriate torque
By double
checking torque5 210
Make sure assembly
instructions are followed
High bearing
clearance5 Use certified parts By measurements 5 175
Monitor bearings vibration
regularly
Unbalance 4Balance system
during assembly
By double
checking balance5 140
Follow standardized methods
when balancing parts
Misalignment 4Align components
during assembly
By double
checking alignment5 140
Follow standardized methods
when aligning parts
Faulty assembly 5Follow assembly
instructions
By double
checking assembly6 210
Make sure foreign objects
cannot impact components,
set up protective covers
Faulty design 2 Use certified parts By measurements 5 70 Buy certified components
Faulty design 2 Use certified partsBy vibration
measurements5 90 Buy certified components
Material fatigue 2Examine parts
before assembly
Through visual
inspection and/or
perform NDT test
7 126 Buy certified components
Faulty assembly 5Follow assembly
instructions
By double
checking assembly6 270
Make sure assembly
instructions are followed
Faulty assembly 5Follow assembly
instructions
By double
checking assembly6 150
Make sure assembly
instructions are followed
Damaged shaft
surface6
Visually examine
part
By double
checking surface5 150
Set up regular
inspections schedule
Faulty design 2 Use certified partsThrough visual
inspection5 50 Buy certified components
Damage shaft
surface4 Loss of grease 6 Use certified parts
Through visiual
inspection5 120
Set up regular
inspections schedule
Adapter
sleeve
and/or lock
ring
breaks
9
Grease
contamination5
Increased
vibration7
Structural
damage
Increased
vibration7
Housing
and/or
fasteners
cracks/breaks
9
Increased
vibration6
Increased
temperature7
Damaged
Rollers,
inner/outer
raceways
and roller cage
Damaged
adapter sleeve
and lock ring
Damaged
seals
Support drive
shaft and
impellers
weight.
(radial forces)
and receive
impellers
thrust
(axial force)
Plummer
Block
Bearings
149
Continued from Table 17.
Corrosion 3Use anti corrosive
materials
Through visiual
inspection6 126
Use suitable materials that
handle
operational/enviromental
conditions
Material wear 4 Use durable materialsThrough visiual
inspection6 168 Use durable materials
Material fatigue 3Examine part
before assembly
Through visual
inspection and/or
perform NDT test
7 147 Buy certified components
Blade(s) or other
parts break off2
Examine part
before assembly
Through visiual
inspection7 98 Buy certified components
Excessive load 4 Monitor loadWith automatic
measurements3 84 Continue to monitor load
Foreign object 2 Keep air ducts clean With regular checks 8 112Set up regular
inspections schedule
Misalignment 6Align components
during assembly
By double
checking alignment5 150
Follow standardized methods
when aligning parts
Unbalance 6Balance parts
during assambly
By double
checking balance5 150
Follow standardized methods
when balancing parts
Looseness 6Tighten bolts with
appropriate torque
By double
checking torque5 150
Make sure assembly
instructions are followed
Faulty assembly 6Follow assembly
instructions
By double
checking assembly6 180
Make sure assembly
instructions are followed
Excessive load 4 Monitor loadWith automatic
measurements3 60 Continue to monitor load
Faulty design 3 Use certified partsThrough visiual
inspection5 75 Buy certified components
Increased
temperature4 Metal friction 3
Follow assembly
instructions
Through audible
measurements6 72
Set up regular
inspections schedule
Corrosion 3 Protect materials Through visiual
inspection5 120
Use suitable materials that
handle operational and
enviromental conditions.
(paint metal surfaces regularly)
Material fatigue 2Examine parts
before assembly
Through visual
inspection and/or
perform NDT test
7 112 Buy certified components
Foreign object 3 Use protective coversThrough visiual
inspection5 120 Build protective guards/fences
Excessive load 3 Monitor loadWith automatic
measurements3 72 Continue to monitor load
Faulty assembly 5Follow assembly
instructions
By double
checking assembly5 150
Make sure assembly
instructions are followed
Excessive load 3 Monitor loadWith automatic
measurements3 54 Continue to monitor load
Faulty design 3 Use certified partsThrough visiual
inspection5 90 Buy certified components
Looseness 5Tighten bolts with
appropriate torque
By double
checking torque5 150
Make sure assembly
instructions are followed
8
Increased
vibration6
Foundation
Support all
components
of the fan
Structural
damage
Structural
damage
Geometria
changes
Geometria
changes7
Increased
vibration5
Impeller
Creates
pressure
differance
between
suction and
discharge side
150
Appendix G: Case Study’s, Vibration Analysis Figures
FTP-1: Main Fan 1
Figure 101: Frequency spectrum comparison between July 2019 and May 2020 for the
Fan ND bearing on main fan 1, in FTP-1.
Figure 102: Vibration signals envelope comparison between July 2019 and May 2020 for
the Fan ND bearing on main fan 1, in FTP-1.
151
FTP-1: Main Fan 2
Figure 103: Frequency spectrum comparison between July 2019 and May 2020 for the
Fan ND bearing on main fan 2, in FTP-1.
Figure 104: Vibration signals envelope comparison between July 2019 and May 2020 for
the Fan ND bearing on main fan 2, in FTP-1.
152
FTP-1: Main Fan 4
Figure 105: Fan DE bearing, waterfall graph of the 1 kHz to 30 kHz frequency spectrums,
from July 2019 to May 2020, for main fan 4 in FTP-1
Figure 106: Frequency spectrum comparison between July 2019 and May 2020 for the
Fan ND bearing on main fan 4, in FTP-1