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IN THE FIELD OF TECHNOLOGYDEGREE PROJECT DESIGN AND PRODUCT REALISATIONAND THE MAIN FIELD OF STUDYMECHANICAL ENGINEERING,SECOND CYCLE, 30 CREDITS
, STOCKHOLM SWEDEN 2019
Vibration analysis for predictive maintenance of a rotary pump
Optimal accelerometer configuration based on vibration analysis for cavitation detection of a bi-winged positive displacement pump
GUSTAV OSSWALD
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
Vibration analysis for predictive maintenance of arotary pump
Optimal accelerometer configuration based on vibration analysis for cavitation detection ofa bi-winged positive displacement pump
GUSTAV OSSWALD
Degree of Master of Sience in Engineering, Track in Mechatronics.School of Industrial Engineering and Management
ROYAL INSTITUTE OF TECHNOLOGYIn collaboration with Akzo Nobel Adhesives AB.
Master of Science Thesis MMK TRITA-ITM-EX 2019:435
Vibration analysis for predictive maintenance of a rotary pump
Gustav Osswald
Approved
2019-06-14
Examiner
Hans Johansson
Supervisor
Xin Tao
Commissioner
Akzo Nobel Adhesives AB
Contact person
Donald O’Boyle III
Abstract Predictive maintenance based on condition monitoring uses sensor and system data to prevent
damage, in advance to a failure occurring, allowing for a service to be performed at an optimal
position in time. Condition based predictive maintenance estimates time of system failure based
on a priori information, which has shown to be much more cost effective than traditional
maintenance methods. Typically, there are applications where either of the maintenance
methods, reactive, preventative or predictive maintenance, prove most sufficient. The expensive
downtime in industrial processes and systems has come to focus development of predictive
maintenance which often is found to be the optimal solution in these settings. In order for a
predictive maintenance algorithm to be developed, there has to be in depth knowledge about the
system and big data to base the algorithm on.
This project is aimed at analysis and condition monitoring of the AkzoNobel -
intelliCURE separate spreader used in the lamella and beam industry. Specifically targeted at
the detection of cavitation in the transportation pumps, which is a common destructive
phenomenon occurring in pumps. Cavitation which is the formation and implosion of cavities in
the liquid, produces excessive shock waves resulting in vibrations. Depending on severity,
cavitation can, in time, lead to internal damage and cause leakage. The type and amount of
cavitation in a system is dependent on the rotational operating speed, which results in lower
amount of vibrations for slow speeds. Where low amount of vibrations presents challenges of
measurability.
In order to detect the low amount of vibrations before severe cavitation development the
optimal solution of measurability must be applied. Therefore, the research investigates the
optimal solution for cavitation detection in terms of accelerometer configuration based on sensor
position and sensor type in relation to reliability.
The results of the study found that the fault mode vibrations caused by cavitation were
detectable at lower rotational speeds than what industry recommended as the limit for an
implementation. Additionally, the position and sensor type results in different performance to
detect cavitation at slow pump speeds. An optimal configuration was found for the specific use
case of the rotary bi-winged positive displacement pump.
Examensarbete MMK TRITA-ITM-EX 2019:435
Vibrationsanalys för prediktivt underhåll av en rotorpump
Gustav Osswald
Godkänt
2019-06-14
Examinator
Hans Johansson
Handledare
Xin Tao
Uppdragsgivare
Akzo Nobel Adhesives AB
Kontaktperson
Donald O’Boyle III
Sammanfattning Prediktivt underhåll baserat på tillståndsövervakning använder sensorer och systemdata för att
förhindra skador, innan fel uppstår, vilket möjliggör att schemalägga en service vid en optimal
tidpunkt. Tillståndsbaserat prediktivt underhåll estimerar tidpunkten för när fel uppstår i system
baserat på a-priori information, som har visats vara mycket mer kostnadseffektiv än traditionella
underhållningsmetoder. Typiskt finns tillämpningar där vardera av underhållsmetoderna,
reaktivt-, förebyggande- eller prediktivt underhåll kan vara bäst lämpade. De dyra driftstoppen
inom industri har lett till fokus på utveckling av prediktiva underhållsmetoder som ofta är ansett
som den optimala lösningen i dessa förhållanden. För att en prediktiv algoritm ska kunna
utvecklas krävs djup kunskap om systemet och stor mängd data att basera en algoritm på.
Projektet fokuserar på vibrationsanalys och tillståndsövervakning av maskinen
AkzoNobel - intelliCURE strängspridare som används i lamell- och balkindustrin. Forskningen
är specifikt inriktad mot att upptäcka kavitation i transportpumparna som används i
applikationen, där kavitation är ett relativt vanligt förekommande destruktivt fenomen som
uppstår i pumpar. Kavitation, som är formandet och implosion av håligheter i vätska, leder till
chockvågor som resulterar i vibrationer. Beroende på intensitet av förekommande kavitation, så
kan det i tid leda till interna skador och resultera i läckage. Typen och intensiteten av
förekommande kavitation i systemet är beroende av rotationshastigheten, som leder till lägre
vibrationsgrad för lägre hastigheter. Där låg vibrationsintensitet presenterar utmaningar i
mätbarhet.
För att detektera den låga graden av vibrationer innan allvarlig kavitation utvecklas måste
en optimal lösning för mätbarhet användas. Därav undersöker denna forskning en optimal
lösning för att upptäcka kavitation med hjälp av accelerometerkonfiguration baserat på
sensorplacering och sensortyp samt hur det relaterar till pålitlighet.
Resultatet från studien visar att mätbarheten av vibrationerna som uppstår vid lägre
hastigheter är bättre än vad som rekommenderas som gräns för användandet av vibrationsanalys
på industriella applikationer. Samt att positionen och typen av sensor resulterar i olika
möjligheter att detektera kavitation vid låga pumphastigheter. En optimal lösning hittades för det
specifika användningsområdet av en dubbelvingad vingrotorpump som studien applicerades på.
Acknowledgement
The project resulted in great learning and provided interesting opportunities regardingcondition monitoring, vibration analysis and predictive maintenance. I would like toexpress my special appreciation and gratitude to the following for their contributions tomy research and this thesis:
Associate Professor Hans Johansson for his discussions and acting as examiner of thisthesis.
Ph.D. candidate Xin Tao for her research insights and great supervision, resulting in adeeper study and additional learning.
Researcher Fredrik Asplund for his guidance regarding research methodology and re-search methods.
Researcher & Lab Chief Ulf Carlsson for his assistance of data interpretation and guid-ance regarding vibration analysis.
Akzo Nobel Adhesives AB and the machine department for providing the opportunity tocarry out this thesis at an industrial cooperation and granting the resources and supportregarding the application and pump malfunctions.
Andreas Ritola for his supervision, close collaboration and guidance of industrial pro-cesses and equipment.
Donald O’Boyle III for his great supervision, assistance and guidance of software andapplications.
PCB & Omniray AB for their guidance of accelerometers and borrowing of a lab-sensor.
ifm electronic ab and Ingemar Sjöberg for their interest and guidance of both hardware,software and the sponsoring of a signal conditioner and sensors.
Contents
Contents
1 Introduction 31.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Maintenance methods . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.2 Vibration analysis of pumps . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2.1 Predictive maintenance . . . . . . . . . . . . . . . . . . . . . . . . 61.2.2 Cavitation & vibration analysis of pumps . . . . . . . . . . . . . . 61.2.3 Industrial application . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.4 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.5 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.6 Ethical & sustainable considerations . . . . . . . . . . . . . . . . . . . . . 131.7 Report outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Frame of Reference 152.1 Industrial maintenance approaches . . . . . . . . . . . . . . . . . . . . . . 15
2.1.1 Reactive maintenance . . . . . . . . . . . . . . . . . . . . . . . . . 152.1.2 Preventative maintenance . . . . . . . . . . . . . . . . . . . . . . . 162.1.3 Predictive maintenance . . . . . . . . . . . . . . . . . . . . . . . . 172.1.4 Condition monitoring techniques . . . . . . . . . . . . . . . . . . . 182.1.5 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Pump characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.2.1 Rotary positive displacement pumps . . . . . . . . . . . . . . . . . 212.2.2 Failure modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.2.3 Cavitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Accelerometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.3.1 Accelerometer position . . . . . . . . . . . . . . . . . . . . . . . . . 282.3.2 Accelerometer type . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4 Vibration Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.4.1 Time domain analysis . . . . . . . . . . . . . . . . . . . . . . . . . 312.4.2 Frequency domain analysis . . . . . . . . . . . . . . . . . . . . . . 32
2.4.3 FFT and normalized FFT . . . . . . . . . . . . . . . . . . . . . . . 33
3 Implementation 353.1 Laboratory test specification . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1.1 Test rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373.1.2 Sensor data acquisition . . . . . . . . . . . . . . . . . . . . . . . . 373.1.3 Tested accelerometers . . . . . . . . . . . . . . . . . . . . . . . . . 383.1.4 Signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 Preliminary tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.3 Case study and test specification . . . . . . . . . . . . . . . . . . . . . . . 403.4 Evaluation of tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4 Results 474.1 Preliminary test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.2 Case study results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.1 Results 30 rpm study . . . . . . . . . . . . . . . . . . . . . . . . . 524.2.2 Results 50 rpm study . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3 Optimal accelerometer configuration . . . . . . . . . . . . . . . . . . . . . 59
5 Discussions and Conclusion 615.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1.1 Accelerometer configuration evaluation . . . . . . . . . . . . . . . . 625.1.2 Cavitation detection . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6 Future recommendations 696.1 Predictive maintenance development . . . . . . . . . . . . . . . . . . . . . 696.2 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Bibliography 72
A Results from Case study at 30 rpm 78
B Results from Case study at 50 rpm 80
C Time domain data after HP-filter from Case study at 30 rpm 82
D Time domain data after HP-filter from Case study at 50 rpm 84
Nomenclature
Abbreviation Description
BEP Best Efficiency Point
BPF Band-Pass Filter
CAD Computer Aided Design
CPM Cycles Per Minute
CPS Cycles Per Seconds (Hertz)
DAQ Digital Acquisition Device
DFT Discrete Fourier Transform
DWT Discrete Wavelet Transform
FFT Fast Fourier Transform
FFTW Fastest Fourier Transform in the West
FIR Finite impulse response
HFFT Hexagonal Fast Fourier Transform
HPF High-Pass Filter
ICP Integrated Circuit Piezoelectric
IEPE Integrated Electronics Piezo-Electric
IIoT Industrial Internet of Things
MATLAB Matrix Laboratory
MEMS Micro Electro Mechanical Systems
ML Machine Learning
NPSH Net Positive Suction Head
1
NPSHa Net Positive Suction Head available
NPSHr Net Positive Suction Head required
RMS Root Mean Square
RPM Revolutions Per Minute
2
Chapter 1
Introduction
An introduction is given to the study, the subject and the over all application the study isbased upon. Initially the Background section 1.1 gives an introduction to how vibrationmonitoring is useful, industrial maintenance methods, condition monitoring and vibra-tion analysis for pumps. Followed by the Purpose section which describes an applicationarea for the research and further specifies the purpose behind the research. Thereafter,a statement and formulation of the research question is presented together with the de-limitations and the methodology that was chosen for the research. The chapter endswith a brief description to the other following chapters in the report.
1.1 BackgroundIn this section a brief description is given on the background of the subject of maintenancemethods such as predictive maintenance and vibration analysis of pumps with causes offailure such as cavitation. Further detailed information of the subjects are presented inchapter 2.
1.1.1 Maintenance methodsOne of the currently biggest industrial changes is the Industrial Internet of Things (IIoT)and industry 4.0 which provides the means to gather and analyze data from systems allaround the world [48]. Mass data collection and analysis has led to the refinement ofhow processes are streamlined and automated. A hot topic in today’s research is pre-dictive maintenance where companies and institutes use big data to develop predictivealgorithms that contrast sharply with older proactive and reactive techniques. Thesealgorithms present a prediction of component and system tear or breakdown resulting inadditional information of when maintenance is needed to prevent production downtime[22].
Current maintenance methods used in industrial companies are reactive maintenance,preventive maintenance and the newer and often more effective predictive maintenance
3
[3]. Predictive maintenance algorithms are based on statistics, Bayesian models, machineor deep learning methods. In turn, predictive maintenance can be based on either logsand alarms or on sensor data where the sensors present the condition of operation, namedcondition monitoring. These methods and corresponding assemblies use the sensor datato develop a span of what data is to be considered as normal operation, which providesthe opportunity to recognize anomalies in the system. These predictive maintenancealgorithms usually find the signs of mechanical wear and other types of approachingfailures before they occur [3][22]. In order to perform any predictive maintenance orfault detection in a system, a large amount of historical data is needed to implement thestatistical or machine learning predictions. Although it might not always be possibleto gain information from a predictive maintenance model due to numerous reasons, onecan still gain insight into the workings on a piece of equipment by studying its logs,alarms and sensor data [40]. The use of events, alarms and condition monitoring basedon logged sensor data and process parameters pose many challenges and have not yetbeen fully explored [40]. Numerous sensors and their corresponding data have shownto give relevant information, which can be used to estimate the health or lifetime ofcomponents. Specifically, it has been shown, that condition monitoring with differentsensor data can give insight regarding the health of a pump. Condition monitoringbased on vibration measurements with the use of accelerometers has been shown to beone of the most prominent of methods, as vibrations occur in a system as a result of thedevelopment of a failure. A further presentation of the different maintenance methodsare given in chapter 2 section 2.1.
1.1.2 Vibration analysis of pumpsVibration analysis has shown to be a valuable tool for condition monitoring of machinery,enabling the use of predictive maintenance. Vibration analysis is considered as one of themost relevant monitoring technique for pumps, due to the rotational movement causingvibrations in the system [7]. The intensity of vibrations correlates to the operationalspeed of the pump, which poses challenges of measurability.
Due to the intensity of vibrations in slow speed applications it is of additional impor-tance to get readings not subject to interference from the applied accelerometers in orderto detect the development of failures. This additional importance of quality readings arecorrelated by not only the position of the accelerometer but also the way its mounted aswell as many other factors.
In order to monitor the condition of a system, the failure modes must be profiled[8]. For rotary pumps, there are many failure causes such as cavitation, poor lubricationbetween seal faces and bearing failure vibrations. Typically these are the main causesof leakage at seal surfaces [8]. Leakage at the axial seals are heavily affected by thevibrations in the system.
Cavitation is a phenomenon where formation of cavities (bubbles) develop withinthe fluid. These cavities later implode causing a shock wave transmitting and causingvibrations in proximity to the implosion. Depending on the extent of the present cavi-
4
tation it can cause serious damage to pumps [30]. If the implosion is in close proximityto a mechanical surface, cavitation erosion phenomenon may easily occur, resulting in alower component lifetime [50][8]. Such damage can greatly reduce the lifetime of sealsand other internal components [8].
Figure 1.1: Example of blistering on a bearing to the left and erosion on a gasket tothe right, possibly caused by cavitation.
A simplified explanation of the requirement for cavitation development could be de-fined as follows: if there is more liquid leaving the pump faster than the suction side candeliver, cavities can form and implode. The low pressure at the inlet results in partialevaporation causing formation of vapour bubbles in the transported fluid. Cavitationresults in a reduction in suction pressure, reduced efficiency, increased temperature,vibrations in the system and wear to mechanical components and thereby reduced com-ponent lifetime [24]. Thereof, it is essential to detect and avoid cavitation developing inrotary pumps early to increase the reliability of the pump [50].
Many pumps are forced to operate outside of their range of best efficiency points,resulting in systems designers going to great lengths to ensure that cavitation bubblesdo not collapse within the pump, but rather after leaving the pump outlet [24]. In orderto investigate and study the role of cavitation in pump applications, the cavitation andvibrations caused by the phenomenon has to be monitored.
In addition, if cavitation is monitored over a longer period of time, the correlation ofhow different excitation levels of cavitation correlates to the machine health or lifetimeof a component. This is an example of an application of condition monitoring. Wherecondition monitoring enables the development of predictive maintenance. A furtherpresentation and description of cavitation is given in chapter 2 subsection 2.2.3.
1.2 PurposeIn this section a presentation is given to the purpose of the project where an industrialapplication lay the foundation of the interest in the corresponding accelerometer con-
5
figurations involved in the research. A motivation is thereby given to the interest inaccelerometer type and position.
1.2.1 Predictive maintenanceIn many industrial applications, predictive maintenance has been proven to be more effi-cient than the more usually applied maintenance methods, such as reactive maintenanceand preventative maintenance. Reactive and preventative maintenance are less efficientsince it results in longer downtime due to repairs of equipment or extensive additionalmaintenance. In order to develop a predictive maintenance algorithm, there would haveto be historical data of the failure modes that shortens the lifetime of components. Pre-dictive maintenance can in turn be based on condition monitoring, where sensors areused to monitor the component health. It is in this area that vibration analysis oftenoutperform other methods since vibration is one of the first signs to develop when afailure mode occurs.Finding the optimal configuration for the accelerometers is a prerequisite for conditionmonitoring in order to early detect the development of a failure. Where poor installa-tion of accelerometers to machine components unfortunately is a rather common issueresulting in the failure not being predicted as early as it could be. In order to addressthis issue the study focuses on the accelerometer type and position for a specific usecase. In order for an algorithm to correctly classify new data as either normal operatingconditions or cavitation development there has to be a clear difference to the data, theevaluation of data is later presented in section 3.4. The motivation is that the posi-tion, mounting and type of accelerometer are prerequisites for good measurements andthereby also prerequisites to develop a good predictive maintenance algorithm. Theearlier the signs of cavitation development within the system could be detected and thebigger difference the data from cavitation compared to normal operating conditions thebetter a predictive maintenance algorithm will perform.
Although development of a predictive algorithm was not within the scope of the the-sis, preparation, storage of data to prepare for predictive maintenance was part of thethesis work. Where a collection of data that shows clear difference between normal andfaulty conditions are of focus.
1.2.2 Cavitation & vibration analysis of pumpsVibration analysis is considered an established method for health monitoring of mechan-ical components or machines, which can be used for predictive maintenance. Studies ofvibration analysis have been done on different kinds of process pumps to monitor inter-nal damage in the pump. The limitation of the method is directly dependent on theintensity of vibrations caused by the failure modes and the operational speed of thepump. Where the speed has to be over a certain threshold for the vibrations caused bythe failure modes to be detected. Low amount of vibrations enhances the challenge ofdetecting failure modes. Additionally since no sensors can be considered ideal due to
6
their unique frequency responses and resonance, a multiplicity of acceleromter must betested where the type of is of relevance to the measurability.
The vibrations caused by failure modes are unique in amplitude and frequency de-pending on what caused them. The defect vibration could, for example, occur periodi-cally, dependent on the rotational speed, be random in nature or have high/low frequencycontent. The types of signal processing are dependent on these vibration characteris-tics. Where cavitation both occurs periodically and is random in nature, the noise ofthe vibration signal lay within the high frequency domain approximately 4-25kHz. Vi-brations from other failure modes such as poor lubrication or bearing failures are ofperiodic characteristic and are dependent on the rotational speed, these usually appearwithin the low frequency domain for slow speed applications, which are hard to detectwith the normally used piezoelectric accelerometer for slow speed applications since theIEPE sensors does not provide the option to measure frequencies down to 0Hz due toaccelerometer type functionality. Since the cavitation occurs in different parts of thepump, the position of the accelerometer is of relevance as the location of the sensorshould be in close proximity to the vibration source, in order to get the best signal forcavitation detection the position is of interest.
The research focuses a target of optimal accelerometer configuration in terms of typeand position to increase the reliability of the system. The result should conclude the bestperforming configuration in terms of reliability of cavitation measuring and detectionat low speed applications. The main objective of this thesis is to develop an optimalsolution for a predetermined application of vibration analysis that is not dependent onhigh operational frequency (pump speed).
Vibration analysis research has shown multiple areas of interest, such as signal pro-cessing for feature extraction and noise reduction of for example propagating vibrations,or anomaly detection in real time using machine learning.
1.2.3 Industrial applicationThis research examines the possibility for condition monitoring and predictive mainte-nance of the AkzoNobel intelliCURE separate spreaders process pumps, the machine canbe seen in figure 1.2. Thereof AkzoNobel are in the early phase of developing predictivemaintenance, IIoT and industry 4.0. This could help them provide the most efficientmaintenance for their customers and their machines.
7
Figure 1.2: The industrial application, AkzoNobel intelliCURE seperate spreader.
The correlation of how specific sensor data could prove useful for fault detection inpumps, specifically the rotary bi-wing positive displacement pump is an area of interest.This type of pump is used in the separate spreader, where two identical copies trans-port glue and hardener from their respective tanks to two separate application nozzlesspreading the glue or hardener on lamellas. One of the most common failures in thiskind of pump is leakage at the axial seal [8], which is verified by experts at AkzoNo-bel. Normally these seals have a rather short lifetime considering the pumps being usedwithin production systems [8]. The seal in this particular application has a range oflifetime from 1-18 months heavily dependant of the operating conditions.
One of AkzoNobel’s problems is the lack of reliable automated monitoring of a faultyaxial seal in the pump for their application. A damaged seal can lead to potential lowvolumetric leakage of glue or hardener along the axis connected to the motor and gearbox.Where as little as one droplet per day, in turn could lead to production downtime, ifthis fault isn’t manually detected. A collection of adhesive inside the gearbox could leadto the destruction of the gearbox or motor. Although the broken component is a loss initself the larger economical impact is caused by production downtime.
In order to predict when a fault occurs one has to indirectly examine failure causes,also denoted as failure modes, that can lead to leakage, such as cavitation, poor lubrica-tion and bearing vibrations. If the failure causes can be examined with sensor data, thena predictive algorithm can be developed with this data to find patterns of the conditionmonitoring. An algorithm like this could in turn predict how the lifetime of the seal
8
changes when the different failure causes are active for a certain amount of time in thesystem. Real time analysis of vibrations and spectral frequency analysis in a systemhas been successful to detect and examine the most common of these failure causes,such as cavitation [32][44][42]. Where the Fast Fourier Transform (FFT) is a useful toolfor transferring real time data to frequency domain. Earlier research has stated thatvibration analysis and signal processing is successful for cavitation detection. The mostrelevant damaging failure mode to monitor is cavitation since the phenomenon and theoccurring vibrations have a big impact on the lifetime of the axial seal. Some other errormodes that are a negative factor to the lifetime of the seal are poor seal lubrication,bearing vibrations, misalignment of bearings or shaft, imbalance of shaft or impeller.The position of the accelerometer has been shown to impact the readings and therebyalso the reliability and maintainability [42]. Since it is known that vibrations propagatesthrough structures, another interesting area is how machines mounted on the same basecause a negative effect on component lifetime. As in study [4] where they are exam-ining two pumps mounted 15 cm apart and how the vibration from one pump can bedetected by the accelerometer mounted on the other pump [4]. Even though the researchin [4] was not within the focus of this research scope it was still an interesting area forthe application of the tested pump which was similarly tested for the application understudy. A major concern for cavitation detection in a slow speed application is using anoptimal accelerometer configuration, as the slow speed results in low intensity of vibra-tions making it harder to detect development of failure modes than for stronger signals.Therefore the research examines the possibilities of detecting developing failure modesat low operational speeds of a rotary bi-winged positive displacement pump.
The research focuses on examining vibrations with one or multiple high-bandwidthaccelerometers, FFT and vibration analysis to detect the common failure mode cavitationthat in turn causes leakage at the axial seal for a rotary bi-winged positive displacementpump. These main causes have been shown to be, cavitation, poor lubrication and bear-ing vibrations [8][44]. The following research questions were derived from the problemformulation.
1.3 Research QuestionsThe industrial goal of preparation for predictive maintenance was set, where the pumpscorresponded to the majority of maintenance. In addition pumps are heavily researchedby different institutes and companies. Where research examining cavitation detectionhas not yet been fully explored as there are numerous recent articles within the subject[44][9].
Given the industrial challenges and recent research advances the following main re-search question was defined.
• What is the optimal configuration of accelerometers in terms of type and positionin regard to reliability when detecting cavitation in a slow speed application of arotary bi-winged positive displacement pump?[8][30][42]
9
Hence an explanation for the criteria for how an optimal accelerometer configurationsolution was evaluated is given. Since the work involves preparation for predictive main-tenance, the result from the study should find the best position to classify a signal asnormal or cavitation during current operating conditions. Therefore the optimal solutionis determined by the difference between a signal from normal operating conditions anda signal from faulty operating conditions (cavitation in the system). The evaluation wasdone both with manual spectral analysis and vibration anaylsis. Values of interest inthe evaluation was comparing the peak value of the cavitation noise from time domainand the energy for an interval where the high frequency cavitation noise was present.Further description of the implementation is given in chapter 3 where the method ofevaluation is further presented in section 3.4.
The second research question was defined in order to validate the use of earlier appliedmethods to a different type of pump at slower speeds.
• What similarities of cavitation detection using vibration spectral analysis exist be-tween the accelerometer data from a gerotor pump and centrifugal pump comparedwith a rotary pump at slower speeds?
The study was focused on the structural vibrations generated in the pump fromnormal running conditions in comparison to the occurrence of cavitation. The optimalaccelerometer configuration in terms of type and placement are evaluated based on thedifference between the signals from normal versus cavitation operating condition. Wherethe comparison of cavitation detection between a slow speed application of a rotary pumpwas tested and compared to the results of the earlier research in [8][9].
1.4 DelimitationsTo narrow the scope of the project, some limitations were determined. The main entriescan be seen in the list below with further description presented in the following para-graphs.
1. Cavitation was the only failure mode examined.
2. One type of pump was examined.
3. No more than two accelerometer types were tested.
4. No more than 9 positions were tested in their corresponding directions.
5. Maximum amount of data analysis was set to one packet per accelerometer con-figuration.
6. Each data packet contained 20 seconds of raw data.
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7. The sample rate was set to 50kHz resulting in a frequency spectrum of 0-25kHzafter transformation.
8. Only FFT, normalized FFT and simple filters such as High-Pass Filter (HPF),Band-Pass Filter (BPF) were examined, no other transformers were evaluated.
9. There was no tests to validate that the vibrations were damaging to the pump orits components.
10. No confirmation was given to the type or amount of cavitation formation in thepump.
Firstly, pumps are affected by more failure modes than what has been explicitly dis-cussed previously (cavitation, poor lubrication, etc). A study of all the failure modeswould be too large for a thesis project to carry out due to the extensive testing re-quired for each failure mode. Some examples of failure modes that was excluded fromthe research are poor lubrication, bearing vibration, misalignment, imbalance of axis,imbalance of impeller and mechanical looseness.
The tests were only carried out on one specific type of pump (rotary bi-wingedpositive displacement pump), there was no tests on other pump variations. The studyfocuses on the optimal accelerometer configuration for the specific use case of the pumpin the system application and specifically for cavitation detection. Where only twotypes of accelerometers were tested, these were the Integrated Electronics Piezo-Eleztric(IEPE) and the Micro Electro Mechanical Systems (MEMS) accelerometer.
Only a smaller amount manual analysis was performed, therefore it was limited toonly analyze a maximum of one packet (graphs) of data per accelerometer position. Onlyone size of data packets of 20 seconds worth of data was evaluated with a fixed samplerate. The sample rate was set to Ts = 50kHz, as it is important to have a high samplingfrequency since the signal is prominent in high frequency.
The transform of continuous data to discrete domain was performed mainly withFFT. Performance of the softwares were not evaluated, where the softwares used in thestudy were ifm VES004, MATLAB and its application Signal Analyzer, no comparisonsbetween the options of software was done. The software choice was dependant on theoptions for filtering, transformers and availability.
For examination of how vibrations propagate from one pump to another the studyonly included tests on the existing separate spreader at the AkzoNobel lab. Where theresults from these tests are not included in the report. Adapting the solution from thestudy to an optimal solution to detect more failure modes such as bearing vibrations,miss-alignment, imbalance of axis, imbalance of impellers or mechanical looseness is ofinterest to the industrial market but was not elaborated or evaluated.
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1.5 Research MethodologyEven though a quantitative method would be better suited to prove the earlier researchand to make the results from this project more robust and reliable, the scope demandeda qualitative approach. This means fewer data packets were analyzed, where the testsprovided sufficient data to draw conclusion and discuss the result. As stated in section1.6, manual data analysis and a qualitative approach results in a risk of letting biasaffect the results, discussion and conclusion. In order to reduce the risk and impact ofpossible bias, a second opinion was given to the results.
The structure for the research methodology of the study initialized with a literaturestudy in order to establish what methods to be used and give a clear connection toearlier research. Practical measurements were taken as reference in order to design acase study. The way of evaluation, analysis and conclusions of collected measurementsand the corresponding performance was performed manually. A short and representativepresentation of the methodology in form of an illustration of the steps of methodologyover time can be seen in figure 1.3.
Figure 1.3: An illustration of time series of applied methodology for the study.
From the preliminary literature study a number of methods were decided and as-signed for the practical measurements. Since vibration analysis has been an acceptedfield for such a long time there are many established methods regarding data evaluation.Some methods for signal processing that has shown practical for cavitation detectionin past research are frequency spectral analysis using FFT, packet analysis using Dis-crete Wavelet Transform (DWT), and time-domain analysis using NLAR [44][16][35][33].Where the most fundamental of these methods was chosen, namely vibration frequencyspectral analysis using FFT, in order to focus the research on the type of accelerometerand position in terms of reliability.
As seen in figure 1.3 the preliminary study resulted in practical measurements andpreliminary tests of the available accelerometers. Thereby verifying the chosen methods.The experimental preliminary tests formed the foundation to develop a case study upon.The following case study had its position tested for multiple positions in relation tothe two positions used in the preliminary experimental test. The preliminary practicaltests were repeated for different rotational speeds, starting at 200 rpm, successivelylowering the rpm with decrements of 50 rpm down to the final tested speed of 50 rpm.The positions chosen are based on recent research, industrial guidance and additionallystrategic positions based on cavitation occurrence. A further presentation of the case
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study is given in chapter 3 in subsection 3.3.
1.6 Ethical & sustainable considerationsThe study investigates faults and damaging conditions on a specific pump from an in-dustrial producer with the use of industrial equipment from another company. Hencea disclaimer is given, the examined conditions are not specifically targeted at a certaincompany and are not to be associated with their brand, the conditions generally occursby operation of equipment outside recommended operating range. Additionally the per-formance of their equipment is not to be evaluated or associated with the performancepresented in this research.
Some ethical and sustainable considerations were made in order to enhance the va-lidity of the project. Since the research methodology was decided to be a qualitativeapproach, as mentioned in section 1.5 a second opinion was given to the analysis of data.This was done in order to reduce the risk of bias effecting the result. Even though itdoes not remove the risks it enhances the validity of the study.
Following, the research was a collaboration between the Royal Institute of Technol-ogy and AkzoNobel, a presentation of disclaimer regarding conflict of interest is given.Since there were two aspects of concern within the project, one being the thesis andresearch itself and the other being the interest of predictive maintenance developmentfor the application. In order to address the two interests some tests and work was doneseparately for AkzoNobel, presented in a secondary exclusive report.
Furthermore since the research project has received support and sponsorship in termsof equipment from additional companies a disclaimer is given. Firstly, the authors workincludes confidentiality and has therefore been reviewed before publication. Secondly, themeasuring equipment has been sponsored to the research, this arrangement supportedthe research but resulted in no personal gains or personal interests.
1.7 Report outlineIn order for the reader to get a better understanding about the report structure a de-scription is given to the report outline. From the earlier sections in this chapter a briefintroduction of the research and subject was given.
The following chapter Frame of Reference 2 further presents details of the subject ingeneral. Information about maintenance methods, pump characteristics, failure modessuch as cavitation. A description of the type of pump that was used in the research,vibration analysis and how data could be interpreted is presented. It also specifies thestate of the art which the study has been based on and presents some of the results fromthe corresponding studies.
The implementation chapter gives a clear presentation of what and how the researchexamined. The methods that were used in the research is presented separately. Thelaboratory environment and the machine is presented as the test rig. A presentation ofhow data was collected and handled is presented in the section 3.1.2, sensor data acqui-
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sition. The last section in the implementation chapter, is a presentation of how the casestudy was performed to provide a better understanding of the following two chaptersregarding results and the corresponding discussion and conclusion.
The report ends with recommendations and suggestions for future research and work.This was kept separate to the recommendations for the specific application the study isperformed on.
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Chapter 2
Frame of Reference
In order to grasp the subject, its challenges and results of the study a frame of referenceis given regarding the subject of accelerometer configuration and vibration analysis ofrotary pumps. An explanation is given to how the study correlates to predictive mainte-nance and condition monitoring. Comparisons are given to earlier studies and explainssome of the industrial challenges within the area of condition monitoring and cavitationdetection. Where the chapter presents the most relevant information gathered from thepreliminary literature study.
2.1 Industrial maintenance approachesIn order to understand how the information from the vibration analysis is considereduseful a presentation is given to the three different ways of maintenance methods. Thesemethods are presented in the following subsections, where each of the three have appli-cations where the respective method is considered as the optimal solution. Additionallyto these methods of maintenance, a presentation is given to the definition of reliability asit is essential to the research. A presentation is given to different techniques of conditionmonitoring. Where the study focuses on the area of condition monitoring to be used forpredictive maintenance. An explanation is given to why predictive maintenance is to beconsidered as the optimal solution for the AkzoNobel application.
2.1.1 Reactive maintenanceHistorically, maintenance has been the repair of broken components in different systems.Where a broken component is noticed by machine failure and the component is replacedor repaired. This is typically known as run to failure, corrective or reactive maintenance[41]. A reactive approach for maintenance can still be the optimal solution in terms of amaintenance plan. Reactive maintenance has shown to be the best strategy dependingon three preconditions [7].
1. The breakdown modes must not be or result in dangerous conditions.
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2. The time for repair is short and the cost is small.
3. The production disturbance from component inefficiency and/or production down-time due to additional repair must be small.
A pencil used in an office is a good example on a component for when a reactive main-tenance is the optimal strategy, it is used until the ink runs out and is then repaired orreplaced.
Reactive maintenance is not an optimal solution for the pump application in thisstudy since the time for repair is rather long and the cost large. Additionally the pro-duction disturbance for a failure might result in complete production downtime and istherefore very costly.
2.1.2 Preventative maintenanceThe ways of managing maintenance has come to change in the past decades due tothe industrialization, where expenses due to component failure or industry downtimeare more severe than the costs of preemptive component replacement [41]. The answerfor the new needs resulted in what is called preventive maintenance. This is the re-placement or repair of components at predetermined intervals for a typical componentlifetime. Preventive maintenance is considered to be the optimal strategy dependant onthe following five preconditions [7].
1. Unexpected production downtime are costly in comparison to planned interrup-tions.
2. Statistical data shows a clear pattern of lifespan or simple equations present alifetime for a given system operation.
3. The repair parts are available and of neutral cost.
4. Service of the machine restores it to original healthy state.
5. Failure may lead to secondary damages and costly repairs.
An example of when preventive maintenance is the optimal strategy is oil replacementin a motor, is is replaced at a set interval in order to not result in a motor failure.
Preventative maintenance could be a viable maintenance strategy for the applicationin this study since the unexpected production downtime are very costly in comparisonto scheduled maintenance interruptions. Also the repair parts are of neutral or low costif the repair is done in time before damage is done to additional parts. However oneof the preconditions is not met for the application, namely the statistical data does notshow a clear pattern of lifespan and equations does not give a accurate presentation oflifetime, thereby excluding preventative maintenance as the best strategy.
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2.1.3 Predictive maintenanceThe third option to strategy of maintenance is predictive maintenance where a largeamount of historical data is used to estimate time of component failure. A predictivealgorithm could be based either on logs of system operation, alarms of failure or conditionmonitoring. The condition based predictive maintenance strategy uses sensor valuesshowing the operational condition of the machine where the changes can be tracked todetect anomalies in a system and thereby give a prediction of how data fluctuates. Thesemethods usually find an upcoming anomaly in far advance to the occurrence [5][41].Predictive maintenance is considered the optimum choice dependant on the followingfive preconditions [7].
1. The system is critical for production and/or the system is expensive.
2. Repair parts are expensive and not immediately available from storage or supplier.
3. Service interruptions, planned and unplanned result in downtime or inefficiency ofproduction, which are very costly.
4. A failure cause direct or indirect danger to life or health of personnel.
5. Regular maintenance demands expertise and is expensive.
Additionally there is one more condition that should be added to these factors that is notincluded from the sources used to describe these methods. The additional preconditionthat should be included therefore is stated by this study to be;
• When the lifetime is hard to estimate and not closely related to operational timebut rather operational condition.
A good example is machinery used in industrial production for example machinery inpower plants.
All of these maintenance strategies has been shown to provide an optimal strategygiven different preconditions and circumstances of machine application [7]. The optimalsolution for the application covered in this study would deem either preventative main-tenance or predictive maintenance depending on the measurability and how early thedevelopment of failure modes could be discovered. If condition monitoring allows thedevelopment of a predictive maintenance strategy this method would be best in termsof efficiency, maintainability, reliability, sustainability and economical expenses for thetested application. This is due to the application fulfilling the following preconditions; 1,2, 3, 5 as well as the precondition suggested to be included by the author of this study.Therefore the research studies the possibility of condition monitoring on the specificprocess pump used in the application.
If an unexpected production downtime due to component failure occurs before thescheduled maintenance the costs related to the downtime are large. Additionally withuncertain conditions the lifetime of the axial seal is hard to estimate [32], in the pump
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application, lifetime could vary from 1 to 18 months according to the AkzoNobel ex-perts. In some cases customers ignore the recommendations of maintenance thereforeresulting in additional damages and causing a longer production downtime than theoriginal maintenance would result in. It is also hard to determine that the pumps in theapplication does not operate outside the pumps Best Efficiency Point (BEP) at customerlocations. Operating outside the BEP may cause conditions developing cavitation andgreatly reducing the lifetime of components. Predictive maintenance could be based onmonitoring the conditions in a system following is a presentation of condition monitoringtechniques.
2.1.4 Condition monitoring techniquesAs mentioned by the name of condition monitoring it is a way to investigate the conditionof machinery to determine mechanical wear and predict failure. Condition monitoringcan also be used to detect failures that could result in early failure also known as compo-nent infant mortality. As a result condition monitoring can detect and prevent occurringfailures resulting in an increase to the component lifetime. All the different availabletechniques uses trends in data, providing health information about the machine andhelps to detect the faults early or even before occurrence [17].
Figure 2.1: An illustrative example of a typical machine failure and the correspondingwarning signs according to National Instruments [20].
As seen in figure 2.1, the vibrations and harmonic signatures often carry the firstwarning signs that the machine is prone to failure [20]. It has been shown that vibrations
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in some cases provide up to three months notice prior to the actual failure date providinga lead time for prevention of failure [20][42]. Monitoring the conditions and its data withvibration analysis hardware and software helps to early predict failure and scheduleproper maintenance [2].
Even though predictive maintenance has proved valuable many industries are stillto implement both online condition monitoring and predictive maintenance methods. Itis still fairly common for route based condition monitoring where technicians manuallyevaluate the conditions, either by human senses or with the help of instrumentation[20][26][19].
The best and most comprehensive condition based maintenance programs utilizesensor fusion or a variety of sensing technologies, where vibration monitoring generallyis a key component [15][49]. Vibration based condition monitoring has been estimatedto be the most widely used technique with 80% of the parameters measured likely to bevibration based [37]. Although, vibration based condition monitoring does not provideinformation to all sources of failures and is limited to monitor the mechanical conditions.Therefore in order to utilize a more reliable and efficient maintenance program it mustinclude additional diagnostic techniques. Some of the known available techniques are:
• Vibration based
• Process parameter monitoring
• Acoustic based
• Current based
• Thermography
• Tribology
• Visual based
Where vibration analysis and monitoring of vibrations is the most widely used andreliable technique for condition based maintenance [31][37].
2.1.5 ReliabilitySince reliability is a central part in the thesis as a part of the main research question,a presentation of its definition in relation to engineering is given. The term reliabilitycan be defined in multiple ways, one way to describe it is as the ability of a systemor component to perform its required functions under stated conditions for a specifiedperiod of time. Where reliability engineering focuses maximizing the component lifetimeand decrease the risk for early failures [26].
This is closely related to the component lifetime and certainty of system performanceand health, which correlates to condition monitoring and predictive maintenenace.
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Figure 2.2: The reliability bathtub curve showing the change of failure rate over time.
A commonly used tool in the concept and evaluation of reliability is the bathtub curvehazard function which illustratively explains the observed failure rates due to infantmortality, random failures or wear out failures, this illustration can be seen in figure2.2. As the study focuses on detection of cavitation using an optimal accelerometerconfiguration for the stated application, it results in an increase of reliability for themachine as it can help to reduce the infant mortality rate as well as the wear out failures.If cavitation can be detected in an early stage before the level of cavitation causes severedamage to components the reliability of the pump and there by the entire system canbe increased. When an optimal accelerometer configuration is applied it increases thereliability of the system as both the lifetime can be increased and a reduction to theinfant mortality rate of components. For slow speed applications the subtle early changesof system operating conditions are hard to detect where the optimal configuration is aneed to increase the reliability. The reliability of a pump could in terms be increased ifthe failure modes were monitored allowing for development of predictive maintenance,following is a presentation to pumps and their corresponding characteristics.
2.2 Pump characteristicsThere exists many different kinds of pumps with completely different capabilities, whichare better suited for different applications. Some example of industrialized pumps are,centrifugal pumps, rotary pumps, peristaltic pumps, screw pumps and piston pumps.An illustration of functionality for a centrifugal pump is shown if figure 2.3. The fieldof vibration analysis is widely used where the majority of studies were found to beperformed specifically on centrifugal pumps [44][8][32].
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Figure 2.3: The functionality of a centrifugal pump, retrieved from [47].
The application for the pump is of high relevance to the design decision of machineryand what pump to include in the system [32][38]. The type of pump best suited for anapplication is dependant on many factors, example of important factors are the typeof liquid to transport, the distance it should be transported and the volume neededover a specific time frame. Since the pumps operate at different ways it results indifferent types of failure modes occurring at different parts of the pump and differentvibrations in the system, even though some similarities are shared [30]. Earlier studiesof vibration analysis and cavitation detection are typically performed on centrifugalpumps, where the pumps uses the centrifugal force to push the liquid from the inletto the outlet. Centrifugal pumps are the most commonly used pump type by today[38]. These pumps usually operate at high to very high operational speeds and is mostlysuited for low viscosity liquids and large volume transportation applications. Rotarypositive displacement pumps on the other hand which often operate at slow operationalspeeds and are heavily used in the food industry with high viscosity liquids, which arenot equally popular to study.
2.2.1 Rotary positive displacement pumpsThe rotary bi-winged positive displacement pump is a fairly common pump for industrialapplications, making about 10% of the pumps used in industry and can handle highviscosity fluids efficiently [39]. The pump consists of a motor and a gearbox driving aingoing axis to a secondary gearbox. The first gearbox closely connected to the motorhas a gear ratio serving the purpose of torque improvement. The secondary gearboxconnected to the pump housing serves as a connection for the driving single axis totranslate its rotational movement to two axis driving the two impeller wings, given by
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the name, bi-winged positive displacement pump. The secondary gearbox often has nochange in gear ratio but has a main purpose to translate the single axis rotation to a welltimed dual axis output from the gearbox. It is of high importance to keep the timingof the impellers at a fixed setting, since a change of just one gear tooth might causea collision of the impellers. Followed by the secondary gearbox is a number of seals inorder to prevent liquid from escaping the pump housing into the gearbox. On each ofthe two axis entering the pump house an impeller is mounted to move the liquid fromthe inlet to the outlet. The outer part of the pump is covered by a pump housing coverwhich closes the build of the pump. An exploded view of a rotary bi-winged positivedisplacement pump can be seen in figure 2.4.
Figure 2.4: An exploded view of a rotary bi-winged positive displacement pump,retrieved from [46].
The rotary positive displacement pump operates by rotating its impellers in a pre-cisely timed manner, creating lower pressure at the inlet than at the outlet. By therotation of the impellers the liquid is forced into the space between the impeller andthe pump housing. As the impeller turns the liquid is forced out of the pump house asthe impellers connect in the middle of the pump. An illustration of how rotary positivedisplacement pumps operate can be seen in figure 2.5.
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Figure 2.5: An illustration of the functionality of the rotary bi-winged positivedisplacement pump, retrieved from [11].
As described in the earlier section 2.2 each pump type is affected by different failureseven though they share some common failure modes. A fairly common failure for therotary positive displacement pump is damage to the axial seals due to various failuremodes. The seals can be seen in figure 2.4 between the pump housing and the impellers.As a result when these seals are damaged, leakage could occur either leaving the pumphousing or entering the gearbox.
As mentioned in section 2.2, the majority of research has been focused at the morecommonly used centrifugal pump which often operate at higher speeds than the rotarypositive displacement pump [38][44][8]. Since earlier research has not fully explored slowspeed applications, the industry is still not convinced vibration analysis is a viable optioneven for slower speeds [45]. Where article [45] is examining a slow speed application forthe earlier mentioned centrifugal pump. The argument is given that the sensors on themarket are not ideal and can’t measure frequencies down to 0 Hz and that the vibrationsoccurring at slow speeds are too weak to capture or recognice the anomalies from thefailure modes. It was recommended not to consider vibration analysis for slower speedsthan around 100 rpm, since the validity for slow speed applications is considered poorfor many of the failure modes. Following is a presentation to the different types of failuremodes in pumps.
2.2.2 Failure modesEven though there are many different types of pumps, the majority of them share com-mon failure modes, in other words, phenomenon that occurs which leads to inefficiency,failure of a mechanical component or damage to the entire pump. Every failure modeis an unwanted occurrence which may not directly impact the function of the pump.However in a longer term the occurrence of the failure modes without proper action maylead to malfunction such as leakage [24][30][27]. Failure modes can be either hydraulicfailures or mechanical failures where some of the failures may lead to the development ofanther. Examples of problems of hydraulic failures are cavitation, pressure pulsations,radial thrust and suction and discharge recirculation [24]. Some mechanical failures arebearing failure, seal failure, poor lubrication, excessive vibrations and fatigue [28][27].Cavitation which is the failure mode in focus in this study is one of the failure modes
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which results in additional multiple failure modes. When cavitation occurs in a system itresults in uneven flow due to fluid pulsations as a result of being starved of liquid. Whenthe cavities implode it results in excessive vibrations, often propagating through the en-tire structure. The vibrations result in uneven loads, resulting in additional mechanicaldamage to components such as impellers, seals and bearings [24][27].
2.2.3 CavitationCavitation as mentioned both in chapter 1 section 1.1 as well as in previous subsection2.2.2, is an unwanted phenomenon also referred to as a failure mode which occurs in thetransportation of liquids. The phenomenon is the development of cavities or bubbles,which for pumps occurs at the suction port. These cavities are then transported tothe delivery side of the pump where a higher pressure forces the cavities to imploderesulting in a shock wave from each of the cavities and vibration when the shock wavehits structural components [44][24][27]. A visual example of cavitation can be seen infigure 2.6.
Figure 2.6: Cavitation in two different type of rotary positive displacement pumps.
It occurs in pumps when the inlet pressure drops below the vapour pressure forthe media transported by the pump. The condition required for cavitation to develop isdefined as the Net Positive Suction Head (NPSH) available is lower than the Net PositiveSuction Head required which is the opposite of the equation used for designing pumpsystems stated by equation 2.1.
NPSHa > NPSHr (2.1)
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System and pump developers go to great length when designing pump systems toensure that equation 2.1 is fulfilled even when operated outside its best efficiency point(BEP) to prevent cavitation bubble collapse inside the pump. Where centrifugal pumpsare usually operated smoothly within the range of 85% to 110% of its respective BEP[24].
NPSH takes multiple factors into consideration such as the suction piping and con-nections to the inlet, the fluid velocity and the absolute pressure of the transportedmedium [8][24]. In essence, cavitation occurs as a result of reduction in suction pres-sure, which can be the result of a clogged inlet, an increase in suction temperature or aincrease of operational pump speed or flow rate above the levels the pump has been de-signed [24]. If the NPSH margin from available and required is small it heavily increasesthe occurrence of cavitation more frequently, this could be due to design constraints or ifthe system develops a malfunction such as clogging of the suction port of the pump. TheNPSHa is calculated using characteristics from the pump’s inlet nozzle and is therebyindependent of the pump or the pump characteristics which assists system designers toavoid a design which may cause cavitation even though the pump is operated withinBEP. The NPSHr is defined as the calculated amount of NPSH that is required to avoidcavitation in the pump and is independent of the system characteristics [24].
Since the research focuses on cavitation detection it is important to understand thephenomenon to an extent that the different damaging factors occur in different areasthereby being important for the position of a sensor.
The failure mode of cavitation in a system or a pump leads to multiple damagingfactors to a system or pump, as defined in the article [24] the four symptoms are asfollows.
(a) Erosion: Initially when the cavities are formed at the low pressure inlet of the pumpit does not introduce any erosive capabilities. However when these cavities aretransported to a high pressure at the outlet side of the pump they instantly implodeexerting enormous local stress on surfaces in close proximity to the collapse, causingdamage to internal components such as the impellers and surfaces such as the pumphouse casing. Signs of erosion due to cavitation in pumps will appear as pitting dueto the water hammering action caused by the collapse of the cavities. When thebubbles implode they result in a shock wave transmitting and hitting surfaces atthe local speed of sound just like a jet beam of liquid, which can results in surfacedamage if the pressure is higher than the ultimate strength of the material. Damagefrom cavitation in form of erosion on a pump housing cover can be seen in figure 2.7
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Figure 2.7: Erosion damage on the pump housing cover, caused by cavitation in arotary positive displacement pump.
It has been shown that the damage due to cavitation increase rapidly correspondingto the volume of the fluid. The rates of present erosion increase by a factor four whenthe capacity of the shock less flow is raised from 100% to 120%, which is generally1.1 to 1.3 times the capacity of the BEP flow.
(b) Vibration: The vibrations that propagate due to the cavitation are characteristicallyrandom in nature and given as pulsations of high amplitude from the delivery of fluidto the outlet. However it is differently argued in research if these vibration are foundin low frequency range such as 0 to 10 Hz or from the noise propagating as vibrationin high frequency domain with a range from 4 kHz upwards depending on the pumptype, design and amount of cavitation [24][44][8].
(c) Noise: Usually cavitation results in noise in terms of a sharp crackling sound asthe cavities are collapsing under the high pressure. This noise has a correlationwhich can measure the severity of the cavitation. The noise usually occurs in closeproximity to the inlet of the pump. If the crackling noise is random in nature and ispresent with intensity knocks, it indicates cavitation in present in the suction sideas recirculation. This type of cavitation does not lower the efficiency if the NPSHrequirement is met but still generates conditions which can cause damage to thepump. As described there are multiple types of cavitation, these types are classifiedbased on the location of the cavities inception and location for the implosion of thecavities, additionally each type is accompanied with its own range of acoustic noiseemission. Sheet cavitation is the first type, which forms cavities across the vanesurface when the pump is operating close to its designed flow with low pressureat the pump inlet. This type of cavitation results in a broad band noise, withlow amplitude, it typically appears from 2 kHz to 40 kHz depending on the pump
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characteristics [24][36]. The second type is called cloud cavitation, where cavitiesform downstream of the cavity sheet and occurs if the pump is operated outside ofits designed flow with low pressure at the suction side of the pump. Cloud cavitationis the loudest type of cavitation which is said to sound like "pumping gravel". Thistype of cavitation generally appears in high frequency domain, such as 20 kHz to 40kHz. Vortex cavitation is the last type of the three types of cavitation, it usuallyoccurs when the pump operate at very low flows and is a highly unstable form ofcavitation. It usually develops in the inlet back flow regime. Even though cavitiesimplode similarly to the other types of cavitation, it is considered as less damagingsince the collapse of the cavities usually occur away from solid surfaces. This kindof cavitation generates a low frequency pulsating beat in the range of 1 Hz to 4 Hz,this phenomenon is known as cavitating surge.
(d) Efficiency reduction: Since bubbles are created in the passage of liquid from theinlet to the outlet, the volume of the transported liquid is reduced from one sideof the impeller to the other, thus resulting in a reduction of delivered liquid. Thelowered efficiency could be an indication of cavitation occurring in the pump orsystem in advance to fully developed cavitation occurrence. The efficiency of thepump can drop drastically over time to the point where the efficiency is consideredas poor [27]. In some occasions and research attempts it has been found that theefficiency slightly increased moments before initial cavitation occurs. A cause of thisis reasoned to be the reduction of friction at the initialisation of cavitation due tothe separation in the flow, right in advance to the implosion of the caveties [27].
Since cavitation is a rather common phenomenon and is to be avoided in order toimprove component lifetime and maintenance costs, corrective procedures have beendeveloped in order to reduce, avoid or control the damages [24].
If the pump application is to deliver precise amount of liquids over time, initial cavi-tation will result in lower capacity and efficency, forcing the system to operate at higherspeeds, which in turn causes additional cavitation resulting in a more harsh operatingenvironment for the pump and shortening of component lifetime. As mentioned in sec-tion 2.1.4 vibrations ealy shows the signs of developing failures. Where accelerometershas become a common tool to monitor the vibrations in machinery including the failuremode from cavitation.
2.3 AccelerometersAn accelerometer is a type of sensor that can measure the displacement, velocity oracceleration at a given point [26]. There are different types of accelerometers that op-erate with different functionalities at different ways [43]. For instance, an accelerometercan have one or multiple axis giving the opportunity to take multiple orientations ofmeasurements for a certain position.
When selecting an accelerometer for a specific application it is important to chooseone appropriate to its purpose and to the environment it will be exposed to. There are
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a number of factors effecting what technical requirements the application needs, wheresome of the important factors are accelerometer type, sensitivity, frequency bandwidth,self resonance frequency and range. In addition to these factors there are a lot of con-siderations that should be taken into account before deciding on a sensor for a specificapplication.
Some aspects concerning the condition monitoring performance are the position withgood mounting and type of the sensor where both of these aspects results in differentcapability of readings.
2.3.1 Accelerometer positionStudies stated the position was essential for good readings and there was recommenda-tions to where to place them in order to get good readings depending on the failure tobe detected [18][50][42].
The position of the accelerometer is crucial for good measurements, where differentpositions performance could be of good quality, not sense the vibration source at all,result in false positives or hide the signal in noise. Even though the positions high im-portance for a good implementation of condition monitoring, it is unfortunately rathercommon practice with poor accelerometer installation [43]. It has been found that themost suitable position to measure a certain source of vibration is in as close proximity tothe vibration source as possible. It is also important that the axial direction of mount-ing is in accordance to the vibration forces in order to achieve accurate measurements.Different research articles has found clear differences in the measurability and perfor-mance for different positions. According to the conclusions from [10] the measurementsperformance in terms of reliability for the tested application was better on board posi-tions rather than fixed parts [10]. In the article "Pump Condition Monitoring ThroughVibration Analysis" several positions were tested at the inlet and outlet of a centrifugalpump, the author concludes multiple specific pump problems such as cavitation to bedetectable with appropriate implementation of vibration based techniques [42]. Wherethe tested positions in the article are oriented along horizontal, vertical and axial posi-tion at the inlet and outlet of the pump. Focus is given on a "correct" position in regardto detecting bearing failures, but no investigation is given into a optimal position inregard to cavitation [42].
Similarly in article [50] where seven different positions at the inlet, outlet and pumphousing were tested to identify the performance of detecting cavitation for a centrifugalpump. It concludes that vibration analysis can be used to early detect the phenomenon ordevelopment of cavitation in a system which relates to the position of the accelerometer[50].
Additionally in study [23] the researchers states: "Since the vibration due to cavita-tion is the only main concern of this study, therefore the accelerometer is mounted atthe suction port in the radial direction." thereafter proceeds with the position withoutany further consideration to the position.
The industrial producer Hansford Sensors advises their users to mount the accelerom-eter in horizontal, vertical and axial orientation and it should be mounted in close relation
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to the bearing in the respective direction as close to the source of vibration as possible[43].
Another factor that is important to consider is the way of mounting, as all of thepossible mountings allow for small slack and thereby acts as a filter. There are many op-tions for accelerometer mounting, such as stud, screw, adhesive and magnetic mounting.There is a fairly unanimous agreement of what method gives the best response, howeverthere still are discussions on when the different methods are possible to use for highfrequency domain. According to different suppliers the different mounting methods areviable for different measurement ranges. According to accelerometer producer CTC, themounting methods ranges for stud mounts and adhesive mounts in theory are around0-15kHz, they also specifically state that real environments often lower the possibilityof range, resulting in a stud mount range of 0-10kHz and an adhesive mount around0-3kHz [34]. Another accelerometer producer PCB Piezotronics states the performanceof accelerometer accuracy decreases with a logarithmic scale at higher frequencies wherethe noise increases for high frequency where different mounting options have differentperformance of noise reduction [14].
According to the company Hansford Sensors, bad mounting of accelerometers in vi-bration monitoring of industrial applications is surprisingly common in practice [43]. Theunanimous known best performance of mounting techniques is said to be stud mountingwhere the more stiff options give the better frequency response. For adhesive optionsthe same conclusion applies regarding the stiffer connection the higher bandwidth.
2.3.2 Accelerometer typeIn order to achieve good measurements there are more than the accelerometer specifica-tions and position that must be taken into consideration, where the type of accelerometeraffect the measurability. The preliminary literature study showed that the most com-mon accelerometer type in industry of condition monitoring today are the piezoelectricaccelerometers also known as IEPE or Integrated Circuit Piezoelectric (ICP). Theseaccelerometers uses a small mass connected to a spring or piezoelectric crystal and apiezoelectric material to measure the vibrations [20]. When the sensor is exposed tovibration it causes the mass move in its corresponding direction resulting in a change ofthe piezoelectric material were the electrical change can be measured and converted.
Another type of accelerometer is the MEMS sensor, which utilizes technology of mi-croscopic size where the internal components could range from 1 to 100 micrometers insize. They are heavily represented in the consumer market in products such as, smart-phones, smartwatches, video game controllers and many more. Due to the increasingperformance of the MEMS accelerometers and a lowering price, they have come to com-pete not only for consumer products but also for industrial applications [1]. Unlike theIEPE, the MEMS sensor can be used to take physical measurements of frequencies downto 0 Hz, which can be favorable for slow speed applications. Typically, accelerometerswith digital interface are less susceptible to noise than analog accelerometers which is infavour of the MEMS sensor.
The accelerometer type is highly relevant to the topic due to their different ways
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of operation and functionality. The IEPE versus the MEMS accelerometers are of highrelevance where the IEPE sensors have been used for a long period of time and arestill the most widely used. On the other hand the MEMS sensors are rather new tothe industrial market and functionalities allow them to measure for slower speeds due tobeing able to measure lower frequencies. According to the research paper [1] the demandfor wireless sensing nodes is increasing where the MEMS sensor is a viable option. AsIIoT, industry 4.0, predictive maintenance and condition monitoring are growing fieldsand the performance of the MEMS sensors are increasing while its prices are decreasing,the industrial MEMS accelerometer are getting closer to the performance of the moretraditional IEPE or ICP accelerometers. The article found that two of the three testedMEMS sensors achieved good test results with similar performance to the IEPE type,where the big difference was the phase shift of 1-5%, it also states the following threesentences; "MEMS sensors could be a good alternative to standard sensors mainly forwireless implementation as there is no need to carry heavy charge amplifiers, but thechoice has to be made according to specifications and through validation tests. MEMSsensors have also to resist harsh environments using an appropriate packaging. Moreinvestigations with various MEMS accelerometers to understand the future direction forimprovements are being carried out.". This lies as a foundation for this research wherethe performance of a MEMS accelerometer is compared to a IEPE accelerometer forcavitation detection at slow speed applications of a positive displacement pump.
2.4 Vibration AnalysisAs stated in subsection 2.1.4, vibration analysis has shown to be the most prevalentmethod used for the monitoring, analyzing and detecting for a mechanical structure’scondition in real time as well as at specified intervals. This is due to the fast datacollection, signal processing and interpretation [13]
Vibration analysis of taken measurements is a complex and wide field which exploitsmultiple aspects of diagnosing and testing aspects, such as condition monitoring anddefect detection [25][44]. Traditional vibration analysis methodology can be divided intofour main principal domains, these are, time domain, frequency domain, joint domainwhich includes both time and frequency domain and lastly modal analysis [13][18][6].Each of these domains provide specific information on the condition and features ofthe component. Since the research only includes time domain and frequency domain afurther explanation is only given of these two domains and their functionalities.
The analysis of vibration data is considered as complex as the measurements includeall the sources of vibration. Each source can in turn generate multiple profiles of vi-brations, subsequently resulting in excitation and additional resonance vibrations candevelop. The measured signal thereby consists of multiple wave signals into a more com-plex signal, which can be hard to distinguish differences from one signal to another [7][8].Vibration is a result from all mechanical movement, where even a gentle placement of acoffee cup on a table top results in small vibrations propagating through the structure.
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2.4.1 Time domain analysisTime domain is the most fundamental of methods, where vibrations are measured andplotted over time with displacement, velocity or acceleration as amplitude. This givesa clear view of how the vibration change over time in a system. For example if thevibration amplitude increase over time for a set system state of operation, this couldprove that a fault is developing [42][44].
Time domain is useful for the over all evaluation of the system state. It helps tostudy subtle changes to the vibrations in the operation. The downside of time domainanalysis is that it could be hard to detect the distinct patterns or differ fault modes fromone an other. In other words, it is very hard to distinguish the source of vibrations fromthe time domain signal [7][13]. As different sources of vibration results in a superpositionof vibration, creating a more complex signal containing information from all vibrationsources, this results in limits to what can be recognized in a complex signal. Timedomain analysis is typically devoted to peak, average, Root Mean Square (RMS) andenvelope values of the vibration force amplitude.
An example of the vibrations in time domain can be seen in figure 2.8 where theplot shows a complex signal hard to distinguish the multiple sources of vibration from arunning condition.
Figure 2.8: The raw time domain data where the plot shows the complex vibrationsfrom the test rigs normal operating conditions recorded in ifm VES004 software.
Typically, time domain analysis is devoted to detect the integral performance of thetested part. For example if the intention is just to measure a peak or an average levelof vibrations no other domain than time domain has to be considered. Typically for
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condition monitoring, the time domain data is consistently compared with historicaldata. However, since system vibrations often change over time and other sources ofvibration can propagate to the measured point, it complicates the whole method asit is challenging to compare the acquired data with historical data and draw accurateconclusions in such a format [31]. Time domain analysis is a viable option for realtime applications where specific signals carry their features and are relatively easy todistinguish if the environment is controlled [44]. As most data is recorded in time domaina quick analysis of the data can be given in advance of choosing another analysis method.
2.4.2 Frequency domain analysisThe frequency domain can be seen as a spectrum, where different frequencies are portraitalong the x-axis and signal strength of displacement (amplitude), velocity or accelerationalong the y-axis [31][13]. A raw time domain signal is converted to the frequency do-main by a mathematical transform technique also known as any of the Discrete FourierTransforms (DFT) available. The vibration profile is a combination of vibration sourceswhich occur from mechanical movement among others, which are related to frequency[13]. The time domain signal can be seen as a combination of vibration sources occurringat different frequencies. Such sources can occur in relation to Revolutions Per Minute(RPM), Cycles Per Minute (CPM), Cycles Per Second (CPS) or in random mannerknown as noise [7].
Once many signals are prominent, time domain data gets too complex to analyze,demanding a different method to be used. It is in these conditions that frequency domainoutperforms time domain analysis [13]. Other options of analysis for complex signals doexist, such as discrete wavelet transform but is not further discussed in this research.
As can be seen in figure 2.9, signals of different sources which are superpositioned,make it hard to distinguish the signals in time domain, but can still be clearly separatedwith the use of FFT and frequency spectral analysis.
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Figure 2.9: At the left, four different signals where the bottom one is the wavesuperposition, to the right their corresponding frequency spectra.
The frequency domain and the corresponding frequency spectrum is widely usedin vibration analysis where components in a system have different sources of vibrationresulting in different fundamental frequencies [27][7].
Since the frequency domain analysis has been proved viable for similar research[44][8], the decision was taken to analyse the vibrations mainly in frequency domain.
2.4.3 FFT and normalized FFTIn order to grasp data of high complexity from continuous time domain it is common toperform a DFT which allows analysis of the data in frequency domain or discrete time[13]. Discrete-time signals can be obtained by sampling of a continuous-time signal.The amount of samples taken over a given time is known as sampling rate. In order toavoid aliasing the Nyquist sampling theorem should be used. According to the Nyquistsampling theorem stated as equation 2.2 the sampling frequency fs should be at leasttwo times higher than the highest frequency contained in the signal fc.
fs ≥ 2fc (2.2)
FFT is an optimized implementation algorithm for the DFT computation [21]. It is acommonly used tool or method for vibration analysis, since differences to the vibrationsignals from continuous time are hard to distinguish from one and other. The vibrationsmeasured in continuous time signal consist of multiple vibration sources resulting indifferent frequency of the vibrations for different type of vibrations and failure modes.The literature study proved FFT to be a viable transformer for data in similar vibrationanalysis studies [44][8]. A normalization can be done to the abscissa of the frequencydomain where a change of speed does not affect the plot. Resulting in the vibrations
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from a source, occurring at its fundamental frequency still being found at the sameposition for different speeds after the normalization, although this might cause a changeof amplitude [31]. A version of FFT known as H-FFT was initially tested but deemeda poor option for cavitation detection as it resulted in dampening of the high frequencynoise.
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Chapter 3
Implementation
This chapter presents the practical work and how the study was constructed. A presen-tation is given to the test rig, the laboratory equipment, the preliminary tests and thecase study. As described in section 1.5 the preliminary tests were performed as prac-tical measurements to assure the capability of measurement of the chosen method andequipment. This assisted in the design of the case study which consisted of a number ofcombinations further explained in this chapter.
3.1 Laboratory test specificationIn order to plan for a test method, the literature study gave sufficient information ofequipment and methods, thereby creating the specific conditions for the layout of thetest. The laboratory test specifications present the details of the apparatus needed toconduct the research, where a detailed list of the test setup can be seen in table 3.1.
Table 3.1: Test equipment details used to conduct the study.
Test setup detailsPump application: AkzoNobel intelliCURE separate spreaderPump type: Nakakin JO25Pump max speed: 450 RPMTank volume: 50 litersTransported medium: WaterFirst accelerometer type: IFM - VSP003 & CYJV/7 cableSecond accelerometer type: IFM - VSA004Sensor positions: External - pump casingSensor mounting: Adhesive - Loctite Super Glue PrecisionDAQ hardware: IFM - VSE100DAQ software: IFM - VES004Signal analysis software: MathWorks MATLAB & Signal Processing toolboxTest location: Akzo Nobel Adhesives AB Laboratory
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The tests were as mentioned carried out on the AkzoNobel separate spreader wheretwo rotary positive displacement pumps can be individually run. In order to forcecavitation to occur in the system, the liquid flow was reduced at the suction side of thepump by placing different orifice plugs at the connection of the tank outlet connected tothe pump suction side, resulting in a pressure drop, similarly as in study [9]. The orificeplug was designed with CAD and 3d-printed in thermoplastic polyurethane TPU 95A,using Solid Edge ST10, Cura and the Ultimaker 3 Extended. The plug consists of twoparts, the main plug and the interchangeable orifice. The part was constructed in twoparts to allow for a quick change of orifice and save time and material from the printing.The plug and the different orifice of 10mm, 5mm and 3mm can be seen in figure 3.1.
Figure 3.1: The four 3d-printed parts, one plug and three interchangeable orifice partsto control the suction flow and pressure.
During the collection of data the system ran collecting data according to the testspecification at a set sampling rate of 50kHz which is in accordance of the Nyquistsampling theorem presented by equation 2.2. Each accelerometer type and position ofthe sensor was collected for 20 seconds of raw data resulting in 1.000.000 data points foreach test.
Since the laboratory copy was not to be damaged in any way for the tests, stud mount-ing of the accelerometers was removed as an option. Therefore the second best optionfor high bandwidth would be adhesive mounting according to the industrial guidelines.Since the sensors were planned to be moved between multiple positions an alternativeto the adhesive mounting was used. Flat surface nuts were glued to the pumps im-peller housing with a stiff superglue. Allowing fast connection and disconnection of theaccelerometers with a bolt to the adhesive mounted nuts.
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3.1.1 Test rigThe tests were performed in a traditional test environment, in an application setting forthe pumps rather than on a test bench. The test rig consists of a laboratory copy of theAkzoNobel intelliCURE separate spreader. The machine structure is fairly similar to thebench test rigs that has been used in earlier research [29][8]. The machine is constructedwith two parallel and equal transportation loops for glue and hardener. These loopswith equal design are constructed of a tank closely connected to the suction port to thepump. The pump discharge side is followed by a pressure sensor and flow meter leadingby tubing up to a discharge nozzle which transport the liquid back to its tank. In order toremove noise from water impacting the surface in the tank a tube was connected insteadof the traditional sparge. This does not affect the usability in a real environment sincethe viscosity of the glue and hardener helps to protect against noise from the surfacetension in the tank. An illustration of the test rig can be seen in figure 3.2.
Figure 3.2: An illustration of one of the equally designed flow loops in the test rig.
The machine’s main purpose is to apply glue and hardener to lemallas with highprecision. Even though the machine transports high viscosity liquids the testing wasperformed with water as the transported liquid. This results in risk of re-circulation atboth the suction and discharge side on the pump. Additionally since the applicationuses variable speed for precise liquid delivery if cavitation develops it reduces efficiencyand the speed will therefore increase, causing further cavitation development resultingin worse operating conditions.
3.1.2 Sensor data acquisitionThe sensor data gathering is an important aspect to the performance of the measurability,where for example sampling frequency is an important factor to consider when decidingfor a sensor data acquisition method. The tests demanded the processing of large amount
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of data. The packets recorded for each of the tested combinations were 20 seconds worthof data. In order to fulfill the Nyquist sampling theorem the data acquisition recordedwith a sampling frequency of 50kHz since the cavitation signal was estimated to appearwithin 4-20kHz. It was also the lower of the two alternatives of sampling frequency forthe DAQ. The technical specifications for the DAQ can be seen in table 3.2.
Table 3.2: DAQ: IFM VSE100 technical specifications.
Specification MetricsConnecting voltage: 24VCurrent consumption: 200mAAmount of connections: 8 (configurable)
Analog resolution: 12-bitSampling frequency fs: 50kHz or 100kHzMeasure bandwidth: 0-12.000Hz
Communication interface: EthernetProtocol: TCP/IP
The IFM VES004 software exports the recorded raw data with 9 informationalcolumns resulting in a size of approximately 100MB for each csv file. In total therecorded data volume for all tests, preliminary, exclusive tests for AkzoNobel and thetest case resulted in approximately 20GB of stored data. The 12-bit resolution and the20 seconds of recording with 50kHz sampling frequency, giving a total of 1.000.000 sam-ples, deemed viable from the preliminary tests and were therefore used in the case studyaswell.
The VSE100 allows connection by Ethernet to a PC allowing storage of real timedata recording. This dataset of time domain data was exported for each accelerome-ter configuration to a csv file format. The files could then be imported to MATLABwhere altering of the table could be made in order to prepare the data for mathematicaltransformation in form of FFT or importation to the application Signal Analyzer.
3.1.3 Tested accelerometersIn order to evaluate the type of sensor, the research covered the study of two differenttypes of accelerometers from the same producer. The two variations of accelerometersthat was tested were the IEPE and MEMS sensors due to different functionalities andranges. Additional information about the two accelerometers are presented in table3.3. The IEPE sensor used was the IFM-VSP003 which was compared to the MEMSaccelerometer IFM-VSA004, the technical specification of these sensors can be seen intable 3.3. The over all specifications of the two accelerometers are fairly similar inperformance. However, the technical specification had a slight variation between thetwo sensors.
An evaluation was not given to the performance of these particular suppliers sen-sors, the tests were only performed in order to find the general differences to cavitation
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Table 3.3: Table of the two types of accelerometers used in the research.
Name VSP003 VSA004Type IEPE MEMS
Intended use Industrial IndustrialBandwidth (Hz) 1,5-16.000 0-10.000
Resonance Frequency (Hz) around 20.000 above 10.000Sensitivity 100(mV/g) Current loop 0-10(mA@25g)Accuracy ±10% ±0, 2%Range (g) 50 25
detection in terms of accelerometer type.
3.1.4 Signal processingThe data was sampled in the ifm VES004 software where the signals could be storedas raw data and then exported to a csv file. These signals could then be importedinto MATLAB where the accelerometer values were converted to doubles and stored invectors.
Initially for the preliminary practical tests the research included the entire frequencyspectra, even the low frequency signals. It was decided these low frequency signalsto be filtered out since it helps with the scaling of the axis when examining the highfrequency noise from cavitation. This was done with a digital Finite Impulse Response(FIR) equiripple HPF where the stopband frequency was set to 4kHz and the passbandfrequency was set to 4,5kHz.
The transformation of raw time-domain data using FFT was decided to be done withthree available tools, these were the IFM VES004 software which allows FFT and HFFT,MATLAB which uses an open source packet called FFTW [12], and the Signal Analyzerapplication in MATLAB which uses normalized FFT. The HFFT was excluded since itresulted in lowering the cavitation noise signal in high frequency domain that was to bedetected.
The normalized FFT in the Signal Analyzer application deemed valuable to helpnotice the limits to operational speed where the difference in operational condition isstill present and could be detected. While the majority of the transforms were madewith the MATLAB FFT function.
3.2 Preliminary testsInitially some experimental practical measurements were taken in order to give a briefevaluation of the technique and the sensors, described in table 3.4. As the frame ofreference and the general usage of vibration analysis showed that technique is generallyused for applications above 100 rpm and more commonly in the range of thousands rpmfor the centrifugal pump. The tests ranged from 50 rpm, which is lower than the generally
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recommendation of 100 rpm up to a speed of 200 rpm. The lower limit was set to 50 rpmto validate that the cavitation noise in high frequency domain would disappear. Therange was thereby set as 50-200 rpm. Since earlier studies have acknowledged verticaland horizontal positions on the pump housing and the industrial guidelines equallyrecommended these positions, these were the two positions used in the preliminary tests.The orifice plug used for the preliminary tests was the 5mm orifice plug.
Table 3.4: Experimental preliminary tests evaluated as practical measurements.
Pump Speed [rpm] Sensor type Sensor position Orientation Running condition50 IEPE Impeller casing Horizontal/Radial Normal50 MEMS Inlet impeller casing Vertical Normal50 IEPE Impeller casing Horizontal/Radial Cavitation50 MEMS Inlet impeller casing Vertical Cavitation100 IEPE Impeller casing Horizontal/Radial Normal100 MEMS Inlet impeller casing Vertical Normal100 IEPE Impeller casing Horizontal/Radial Cavitation100 MEMS Inlet impeller casing Vertical Cavitation150 IEPE Impeller casing Horizontal/Radial Normal150 MEMS Inlet impeller casing Vertical Normal150 IEPE Impeller casing Horizontal/Radial Cavitation150 MEMS Inlet impeller casing Vertical Cavitation200 IEPE Impeller casing Horizontal/Radial Normal200 MEMS Inlet impeller casing Vertical Normal200 IEPE Impeller casing Horizontal/Radial Cavitation200 MEMS Inlet impeller casing Vertical Cavitation
The practical measurements verified the performance of the accelerometers andmethod, from these results a case study could be designed.
3.3 Case study and test specificationIn order to find the optimal accelerometer configuration for cavitation detection withvibrations analysis, an evaluation of the current trends of vibration based monitoringin the industry was compared with methods used in earlier research. This part of theresearch which was partly based on both recent research and industry trends can betermed as retrospection of previous studies and cases.
The preliminary tests verified the performance of the chosen implementation, result-ing in a better design of the case study. Following the study was focused on the slowerspeed cases of around 50 rpm, since the cavitation noise signal was present even for theslowest speed tested in the preliminary practical measurements.
Multiple positions were subject to be tested and evaluated since many positions havebeen tested by recent research and different positions are given as a recommendation bythe industry. Earlier research as stated in chapter 2 focused positions of the accelerom-eters on the external impeller housing and connections at the suction and discharge sideof the pump. The accelerometer orientation have been tested for vertical, horizontal,
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axial and radial orientation in reference to the pump. Therefore the majority of thepositions used in earlier research was tested in relation to the pump used in this study.In agreement to the research, industrial applications refer to positions on the impellerhousing in the same orientations. However it is stated in both earlier research and in-dustrial guides that the sensor should be placed as close as possible to the source ofvibration. Thereof some additional positions were introduced dependant on the cavita-tion phenomenon presented in section 2.2.3. One of these positions which was decidedto be tested out of curiosity was the position right after the impellers at the dischargeside in an axial direction. This is since it is the closest position to the implosion of severecavitation which can cause surface erosion, as seen in figure 2.7.
Following figures present the tested positions with their respective labeling. Thesepositions are denoted as; T vertical top, H horizontal, A axial, I horizontal inlet, Bvertical bottom, R radial (mixed horizontal and vertical) and O horizontal outlet. Thelocation for comparison of the MEMS and IEPE performance are in close proximity to itsrespective position. However a slight difference to the position of the compared positiondid exist since the mounting technique was decided to be adhesive mounting with nutand bolt. The pump application with some of the earlier labeling of the denotations canbe seen in figure 3.3.
Figure 3.3: Four accelerometers with adhesive mounting using nuts and bolts, at someof the tested positions of the sensors.
The positions are presented at their specified positions in a rendered environmentwith its denotation in figures 3.4, 3.5 and 3.6 which are also presented in table 3.5 forreference. There were in total 9 positions tested for two types of accelerometers resultingin a total of tests a 18 tests per operational speed or orifice plug installation. All tested
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positions have been tested for cavitation detection in recent research except positionsA5 and A6 which was added as interesting positions. These positions were tested forspeeds of 30 rpm and 50 rpm which was shown to be operational speeds carrying thecavitation noise signal.
Table 3.5: Table of the tested positions in the case study.
#Test position Denotation Position Intended sensor type1 T1 Vertical Top 1 IEPE2 T2 Vertical Top 2 MEMS3 B1 Vertical Bottom 1 IEPE4 B2 Vertical Bottom 2 MEMS5 I1 Horizontal Inlet 1 IEPE6 I2 Horizontal Inlet 2 MEMS7 O1 Horizontal Outlet 1 IEPE8 O2 Horizontal Outlet 2 MEMS9 H1 Horizontal Side 1 IEPE10 H2 Horizontal Side 2 MEMS11 R1 Radial Side 1 IEPE12 R2 Radial Side 2 MEMS13 A1 Horizontal Axial 1 IEPE14 A2 Horizontal Axial 2 MEMS15 A3 Horizontal Axial 3 IEPE16 A4 Horizontal Axial 4 MEMS17 A5 Horizontal Axial 5 IEPE18 A6 Horizontal Axial 6 MEMS
The evaluation for these tests was done both by calculating the energy difference be-tween the normal and cavitation condition for the range of the noise and maximum peakcomparison in time domain, which was presented visually. The best visual presentationof the position locations are presented in figures 3.4 to 3.6 where different views of thepump present sight of the different positions.
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Figure 3.4: Top left side view of the tested pump with the denotations at the testedpositions.
Where the positions T1, T2, R1, R2, H1, H2 I1 and I2 can be seen in figure 3.4 attheir corresponding positions from a top left side view.
Figure 3.5: Bottom left side view of the tested pump with the denotations at the testedpositions.
Similarly for the positions B1, B2, O1 and O2 which can be seen in figure 3.5 at theircorresponding positions in a bottom left side view.
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Figure 3.6: Axial front view of the tested pump with the denotations at the testedpositions.
Lastly is a presentation of the positions A1, A2, A3, A4, A5 and A6 which can beseen in figure 3.6 at their corresponding positions from a front axial view.
These were the positions of interest from the preliminary literature study, recom-mendations from industry and proposed by the author, a description is given of howthe data evaluation was done for the signals from the type of accelerometer and testedpositions.
3.4 Evaluation of testsSince the research focused on finding an optimal accelerometer configuration for de-tecting cavitation in a specified low speed application for the rotary bi-winged positivedisplacement pump, it demanded a statement of how the evaluation was done to theresults of finding its optimal solution. The optimal solution was considered as the mostclear difference of cavitation data in comparison to data from normal operating condi-tions, for the same type of accelerometer at the same position. The bigger differencebetween normal data and cavitation data, the better performance of the configuration.This was decided the optimal since a machine learning classification model performs bet-ter if there is a clear difference between the data for the classes. The further apart theclasses or features also known as the condition indicators the easier it is for a machinelearning algorithm to correctly classify a new data sample.
This was measured with both the difference of maximum peak value from time do-main as well as difference in cross spectral density in frequency domain between thesignals from normal operating conditions and the signal from developed cavitation. Themaximum peak value from the time domain indicates the intensity of acceleration at
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the position. This value shows the over all increase of vibration when cavitation occurs,however it is not a good measurement in its own as the source of vibration is not iden-tified in this way. But since the difference of maximum peak value is combined with thedifference in energy for the noise signal, the identification includes both level of vibrationforce and the certainty that cavitation is present.
The maximum peak value was measured in time domain after applying the HPF. Thisresults in a value showing the vibration intensity of the noise. A peak value from the rawmeasurements before applying the filter could be valuable for a predictive maintenancealgorithm but was not considered for evaluating the type of sensor and position sincethe peak value in low frequency domain is not a sure indicator of the cavitation noise.
Power spectral density (PSD) shows the energy from signal strength as a functionof frequency. It is a measurement showing what frequencies correlates to strong signals.It can be calculated as the integral from the start of the differing signal to its end.Comparing two energies from two signals could be calculated by cross spectral density.The PSD is presented by equation 3.1 where the energy is calculated as the integral ofthe time domain signal x(t). According to Parseval’s theorem the energy is equal to thearea under the square of the magnitude of the frequency domain signal seen in equation3.2. Since the FFT shows the PSD of the signal, the energy for a frequency spectrumcould be summed. The cross power spectral density is thereof the distribution of powerper unit frequency.
E =∫ ∞−∞|x(t)|2dt (3.1)
E = Es =+fend∑
n=fstart
X(f) (3.2)
The frequency interval was determined from the presence of the vibration noise fromthe cavitation, this was determined by the preliminary tests to be from 4.5kHz up to15kHz. The energy spectral density is then compared for the two signals in accordanceto equation 3.3 where Ecavitation represents the energy of the signal from cavitation andEnormal represents the energy of the signal from normal operating conditions.
Ediff = Ecavitation − Enormal (3.3)The energy difference between normal and cavitation condition was done for the case
study where speed of 30 rpm with the 3mm orifice and the verification study at 50 rpm.Since the vibrations are directly correlated by the suction pressure and thereby also theorifice diameter, the orifice had to be lowered for the slow speed application in order toboost the vibration forces and the intensity of vibrations from the cavitation.
The signal difference was also given as percentage between the signals, where equation3.4 shows the method of calculation of the difference in percentage between the energyfrom the two signals. This evaluation shows how big the relative difference are betweenthe cavitation signal and the normal operating conditions signal.
Ecavitation% = Ecavitation/Enormal (3.4)
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The energy used for the calculation of the percentage in difference between the signalswas used either over a spectra of 4500Hz to 15.000Hz in order to see the general difference.For the signals that showed a clear peak difference the percentage was taken over thespectrum where the difference of the peak showed present.
An evaluation of the signal difference in percentage is valuable to easily understandhow big the difference is between the signals. However the negative effect of evaluatingthe signal difference with a percentage difference is that the general signal strength islost. Therefore the energy from a PSD calculation is better suited for the evaluation ofan optimal solution to show the difference between the signals. The percentage differencecould be an addition of metric to include for a ML application where multiple dimensionsof metrics could be evaluated. It also shows what positions to be considered as valuableat all. The percentage was given in order to better understand the magnitude of themetrics, even though not considered as a more valuable evaluation than the energydifference.
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Chapter 4
Results
In this chapter the results from the empirical study is presented, analyzed and comparedto results from earlier studies and theory in the field. The field and frame of referenceof the study has been presented with the implementation. Following in this chapter, theresults from the preliminary study and the two case studies are presented.
4.1 Preliminary test resultsThe practical measurements were addressed as described in chapter 3, as a verificationof method and reference to design the case study. The result from the preliminarytests showed that both the MEMS and the IEPE accelerometer managed to detect thenoise that occurs in the stage of cavitation development. The two positions for thesetests were the vertical top position and a horizontal side position on the pump housing.Both accelerometers managed to detect the noise from cavitation for a running speedof 50 rpm, both in time domain and frequency domain. Therefore the study could bedesigned for slower speeds and different positions in accordance to section 2.3, whichpresents different positions used in industry, earlier research and some positions thatwas found to be interesting.
The preliminary study showed that the vibrations that develop as a result of cavi-tation are much stronger than the vibrations that occur at normal operating conditionfor speeds of at least 50 to 200 rpm for the specific type of pump tested. This canbe seen in figures 4.1 and 4.2, where raw data from the IEPE mounted in a horizontalside position and the MEMS accelerometer mounted at a vertical top position indicatesstronger vibrations for cavitation than normal running conditions. It is also clear thatthe vibrations measured with the MEMS sensor at the vertical top position are strongerthan measured at the horizontal side position for these operating conditions.
Since the vibrations are stronger for cavitation than for normal operating conditions,this shows that the difference in peak value for the raw time domain data is a goodmeasurement to separate a cavitation operating condition signal from a normal. But inorder to determine cavitation as the source of vibration, an analysis of the high frequencynoise was needed.
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Figure 4.1: Raw data from cavitation and normal operating state over 20 seconds ofrecorded samples at four different speeds using the IEPE accelerometer at a horizontal
side position.
The cavitation phenomenon for the preliminary tests were done with a 5mm orificein accordance to subsection 3.1.
Figure 4.2: Raw data from cavitation and normal operating state over 20 seconds ofrecorded samples at four different speeds using the MEMS accelerometer at a vertical
top position.
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When the raw signals are transformed with the MATLAB FFT the signal showsa clear difference for the cavitation signal in comparison with the normal operatingconditions. For the IEPE accelerometer mounted at a horizontal side position, the lowfrequency vibrations are stronger than the noise developed by the cavitation in highfrequency domain. The high frequency vibration noise ranges from 5000Hz to 8000Hzand has more energy than the normal state signal. This shows a difference of the noisein high frequency domain between normal operating conditions and the failure modecavitation, this can be seen in figure 4.3. Even though the signal was somewhat differentbetween normal operating condition and cavitation in the low frequency domain, thedifference between the signals is easier recognizable for the high frequency domain noisesignal. The peak value in the high frequency domain also shows a clear difference betweenthe normal and faulty operating conditions.
Figure 4.3: The frequency spectra for cavitation and normal operating state over 20seconds of recorded samples at four different speeds using the IEPE sensor at a
horizontal side position.
The MEMS accelerometer mounted at a vertical top position measures differentlyas the noise appear in higher frequency. For some of the speeds the high frequencynoise signal has a higher peak value than the low frequency spectra. Similarly as forthe IEPE accelerometer the noise signal carries more energy than the other vibrationsources. The noise signal showed present in a range of 5000Hz up to 13.000Hz withdifferent peak characteristics than for the IEPE sensor. Just like the IEPE sensor thereare some differences between the normal operating signal and the cavitation signal in lowfrequency domain, although the characteristic difference for cavitation is carried withthe noise signal in high frequency domain.
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Figure 4.4: The frequency spectra for cavitation and normal operating state over 20seconds of recorded samples at four different speeds using the MEMS sensor at a
vertical top position.
Since the clear difference was verified to be in the noise signal located in high fre-quency domain, similarly as in the study [8] and [9], therefore a high-pass filter wasdecided to be applied at 4000Hz for the future analysis of the noise signal.
Measurement of the vertical top position showed that both the MEMS and the IEPEaccelerometer were capable of showing a minimal difference to the measured vibration forspeeds of 30 rpm. The data was hard to distinguish for speeds of 30 rpm with the FFT,therefore it was analyzed with a normalized FFT plot. The MEMS accelerometer had asmall spike at 0,45 rad/sample while the IEPE sensor could only show small differencesof data at 0,20 to 0,25 rad/sample for the same vertical top position. This practicalmeasurement also showed that signal strength for the MEMS sensor was stronger thanor the IEPE sensor. This was found using a normalized frequency plot and normalizedFFT as seen in figure 4.5. This was later verified and can be seen in the plots in appendixA where a slight differences to the normal and cavitation condition could be noticed intheir corresponding FFT plot.
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Figure 4.5: Raw data and normalized frequency spectra for cavitation and normaloperating state over 20 seconds of recorded samples at 30rpm for both MEMS and
IEPE at a vertical top position.
Thereby it was shown that there are differences of the type and position of the ac-celerometer and that the force of vibrations are related to rotational speed for cavitation,where 30 rpm was set as the limit. With these results the case study was designed forspeeds down to 30 rpm as the limit. A secondary case was designed for a speed of 50 rpmwhich had clearer difference between the two signals cavitation and normal operatingconditions.
4.2 Case study resultsIn this section detailed analysis is given to the case study and the results. Some of thetest plots for different positions are only presented in appendices A and B where themost relevant and best performance are presented in this section.
The evaluation of performance of a position and accelerometer type was done both inregard to its cross power spectral density of the high frequency spectra carrying the noisesignal. As well as the peak value of the filtered time domain signal carrying the noise,as presented in section 3.4. These variables show the differences in vibration intensityof the noise, that differs the normal running condition from cavitation.
The energy difference and peak value difference for all the tested positions and thetwo speeds can be seen in table 4.1 for pump speed of 30 rpm and table 4.2 for speedsof 50 rpm.
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4.2.1 Results 30 rpm studyThe study started with tests for a rotational speed of 30 rpm in order to prove thelimits of the measurability for different positions, where a higher speed study could actas validity to the tests. The case study was performed according to the specifications insection 3.3, where the denotation of the positions used in the tables can be seen on thepump in figures 3.4 to 3.6. Following is a presentation of the evaluated test data fromthe 30 rpm case study. Where the peak and energy difference between the two conditionsignals can be seen in appendices A and C.
Table 4.1: The results from the tested positions in the 30 rpm case study.
Position Type Test Energy Difference Signal difference Peak difference[#] [Amplitude m/s2] [%] [m/s2]
T1 IEPE 1 1.6992 25.81 1.7299T2 MEMS 2 0.4813 0.47 1.5503B1 IEPE 3 0.8610 3.23 0.9153B2 MEMS 4 1.6278 1.62 0.9338I1 IEPE 5 1.7025 19.92 1.1311I2 MEMS 6 2.1822 2.16 2.0040O1 IEPE 7 0.0656 0.24 -0.0335O2 MEMS 8 1.8128 1.79 1.2321H1 IEPE 9 -5.8417 -48.09 -0.2184H2 MEMS 10 -0.2547 -0.25 0.3498R1 IEPE 11 0.7191 25.87 1.1425R2 MEMS 12 1.7917 1.65 -0.0943A1 IEPE 13 3.4510 13.47 1.5963A2 MEMS 14 2.4161 2.38 4.1827A3 IEPE 15 -3.8360 -14.05 0.2256A4 MEMS 16 -0.4518 -0.44 -0.9448A5 IEPE 17 -1.9360 -8.56 0.7133A6 MEMS 18 6.3841 20.88 5.3675
The biggest difference for the signal energy in the spectra was found to be the hori-zontal inlet position I1 and the horizontal axial position A1 for the IEPE sensor and thehorizontal axial position A2 and A6 for the MEMS sensor when the pump operates at30 rpm. However the T1 and I1 positions had very close similarity of signal energy.
The peak value difference for the tested positions showed that the best position forthe IEPE sensor was either vertical top position T1 or the horizontal axial position A1.For the MEMS sensor the peak difference showed that the two horizontal axial positionsA2 and A6 had the best performance of detecting cavitation at slow speeds.
When looking at the percentage difference over the peak spectra it is noticeable thatonly some of the combinations shows a valuable difference between normal operatingconditions and developing cavitation. The configurations relative difference (percent-
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age) shows that T1, I1, R1, A1 and A6 are all positions viable for the detection ofcavitation development. If only the relative difference would be used to evaluate anoptimal solution, then the radial position with the IEPE sensor would be the best con-figuration for the application. Still, the peak and energy difference act as better suitedvariables for evaluation as in the case of having a predictive algorithm built, the absolutevalues give more information valuable for a remaining useful life calculation. If a mix ofthe three evaluation variables would be used the configuration with the MEMS sensorat position A6 shows the biggest over all difference of the three metrics.
The best performance of the combination of the two variables peak difference andenergy difference, would be as far from zero as possible, therefore in order to evaluatethe results a scatter plot was done to visually see the best performance configuration,as seen in figure 4.6, where the numbers in the figure correspond to the number of thetested position and accelerometer type.
Figure 4.6: A scatter plot of the 30 rpm tests, showing the number of the test inaccordance to its variable value of energy and peak difference for the noise signal.
In figure 4.6 it is possible to determine the best configuration. Out of all configura-tions the result showed the MEMS sensor at a horizontal axial position at the deliveryside of the pump A6 was the optimal configuration. Which was also a position less com-mon to use, suggested by this research to be studied. Viable position for the IEPE sensorwould be T1, R1, I1 and A1 has these have a far distance to the origin and they havesimilar values. It is argued that the A1 position might not measure the cavitation signal
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even if the signal, when present is stronger, if a window of 10 seconds worth of data wasused instead of 20 seconds, as can be seen in figure 4.8. While T1 has the biggest abso-lute difference R1 and I1 still shows viable with having a more peak like characteristic,which is easier to determine as a property of the noise of cavitation development.
The case study shows a clear trend that the MEMS sensor and IEPE sensor gener-ally have different performance at different positions. The best position was found tobe in relation to the type for a specific speed application of a rotary bi-winged positivedisplacement pump tested, as can be seen in figure 4.6 and appendix A.
The result of the MEMS sensor at the position A6 to be the optimal configurationwas most likely due to the position being in the closest position of the vibration source ofthe cavity implosion due to the pressure change after the impeller. The risk for internalback leakage between the impellers could also be a contributing reason for A6 to be anoptimal position, as the risk for back leakage increases as the pressure difference risesbefore cavitation develops. This was the reason behind the suggestion of the position tobe studied, where the damage at the position from erosion could be seen in figure 2.7.
The negative numbers in table 4.1 indicates that the value was larger for the normaloperating conditions rather than cavitation thereby not being able to differ the twosignals for a speed of 30 rpm. Even this can be seen in the figure plots from the casestudy of the positions at the operational pump speed of 30 rpm presented in appendixA. The frequency domain plots for the configurations capable of detecting the differencein operating conditions for both the IEPE and the MEMS sensor can be seen in figure4.7 and their corresponding time domain signal can be seen in figure 4.8.
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Figure 4.7: Plots presenting the frequency spectra for cavitation and normal operatingstate over 20 seconds of recorded samples at 30 rpm of the viable positions for both
MEMS and IEPE sensors.
The reason for the close similarity between cavitation development and normal op-erating conditions in the frequency domain, is since the vibration intensity from highfrequency noise for these slow speeds are similar to the background noise. This visu-ally presents the challenge of detecting cavitation during its development stage and theimportance of an optimal sensor configuration since the vibration intensity is so low.
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Figure 4.8: Plots presenting the raw accelerometer values for cavitation and normaloperating state over 20 seconds of recorded samples at 30 rpm of the viable positions of
both MEMS and IEPE sensors.
The result for the IEPE sensor was in accordance to the positions stated by earlierresearch where a top, radial, inlet and axial position on the pump housing was consideredas good positions for the IEPE sensor. The results from the 30 rpm case study showedthat an optimal accelerometer configuration is dependant on both position and by theaccelerometer type. The results of the data can be seen as plots in appendices A andC where the data from cavitation and normal operating conditions were compared forthe different positions and the two accelerometer types. It is clear that the type andposition are important factors to consider when detecting cavitation phenomenon for aslow speed application as the signal is only possible to detect with some of the testedconfigurations.
4.2.2 Results 50 rpm studyThe case study was replicated for another operational speed in order to see the differencesof the performance of the configuration at other speeds. Where 30 rpm was proved as alimit and only some of the positions proved valuable for cavitation detection. The speedwas set to 50 rpm which results in a stronger signal difference between faulty conditionand normal operating conditions due to vibration intensity. Following is a presentationof the evaluated test data from the 50 rpm case study.
The 50 rpm case showed that an optimal configuration is dependant on speed where
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Table 4.2: The results from the tested positions in the 50 rpm case study.
Position Type Test Energy Difference Signal difference Peak difference[#] [Amplitude m/s2] [%] [m/s2]
T1 IEPE 1 18.1064 37.18 3.4168T2 MEMS 2 140.0844 135.67 17.9520B1 IEPE 3 8.4610 14.41 4.3293B2 MEMS 4 217.3736 203.15 27.8728I1 IEPE 5 27.8137 183.38 1.2697I2 MEMS 6 117.3356 114.70 15.8766O1 IEPE 7 14.4856 51.57 -0.0598O2 MEMS 8 89.7844 88.53 6.6868H1 IEPE 9 58.1062 363.92 5.7168H2 MEMS 10 66.3222 63.55 5.9805R1 IEPE 11 17.5421 49.63 2.9246R2 MEMS 12 213.8475 187.83 34.4872A1 IEPE 13 8.2409 15.75 0.0012A2 MEMS 14 215.5583 199.41 17.3774A3 IEPE 15 10.3439 20.63 3.6285A4 MEMS 16 233.6581 219.66 49.6096A5 IEPE 17 17.5808 39.84 3.0605A6 MEMS 18 86.4916 84.51 11.6751
different configurations would be considered as optimal. This could be due to a changeof cavitation type for the 50 rpm study. In accordance to the evaluation values it showsthat all values are significantly larger than for the 30 rpm study since the vibrationintensity is greater at faster operational speeds. An optimal configuration for cavitationdetection at speeds of 50 rpm could be given similarly as for the 30 rpm case. Howeverthis is not presented since the 50 rpm tests were done in order to validate the possibilityof cavitation detection at faster speeds. Even though the positions A4 and R2 or evenB2 would be considered as optimal or suboptimal, these configurations were not capableof clearly detecting cavitation development at 30 rpm. The 50 rpm case presents thedifference in measurability, which results in a greater challenge of cavitation detectionat slow speed applications.
The result shows that all the accelerometer configurations were capable of detectingthe difference of cavitation versus normal operating conditions for a speed of 50 rpm.The only value indicating otherwise was the peak difference of the O1 position usingthe IEPE sensor. It could be argued that the reason of the value difference is since itcould have been cavitation present in the system even during the recording of normaloperating conditions, even if the inlet was not plugged with an orifice. This is since thedata from normal operating conditions indicates cavitation peaks in the raw data seenin appendix D. It can also be seen in the frequency domain plot aswell, where a smallincrease of signal strength is present around 7kHz, which can be seen in appendix B.
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However it can be concluded that the signal strength varies depending on the severity ofthe cavitation. Which is dependant on both the operational speed of the pump aswellas the inlet orifice. The speed and orifice both changes the NPSHa resulting in differenttypes of cavitation and intensity of vibrations.
The only position for the IEPE sensor which performed closely to the MEMS sensorat the same position was the horizontal position H1 which had similar performance tothe MEMS sensor at position H2. Which was also the best position for the IEPE sensorof the tested positions, as can be seen in figure 4.9. However, the configuration of IEPEsensor at the horizontal position H1 is a good example of the configurations from the50 rpm case not acting as optimal in slow speed applications. As the IEPE sensorat the horizontal side position H1 was not capable of detecting the failure mode forslower speeds, where other configurations still recognized the signal difference. The H1position was concluded as the worst position in the 30 rpm case study, which shows thata configuration needs to be evaluated at the slowest limit where the vibration intensitystill recognizes the difference between the operating conditions.
Figure 4.9: A scatter plot of the 50 rpm tests, showing the number of the test inaccordance to its variable value of energy and peak difference for the noise signal.
Equally as for the 30 rpm case it was found that the MEMS sensor generated alarger difference between the two signals from faulty and normal operating conditions.A reasoning to why the MEMS sensor resulted in better detection of the cavitation signalis presented in the next chapter 5.
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4.3 Optimal accelerometer configurationThe research focuses on an optimal accelerometer configuration in terms of type andposition of the sensor for cavitation detection of the tested pump application. Theresearch questions were derived from the problem formulation and the preliminary lit-erature study. Where the main research questions results covering its topic has beenpresented.
• What is the optimal configuration of accelerometers in terms of type and positionin regard to reliability when detecting cavitation in a slow speed application of arotary bi-winged positive displacement pump?[8][30][42]
Hence the best configuration in terms of accelerometer type and position in regardto reliability evaluated based on energy difference and difference of peak value of thehigh frequency noise signal for the two compared signals is presented. The optimalconfiguration was found to be the MEMS sensor located at the axial horizontal positionA6 at the outlet of the pump as it showed the largest difference between the comparedsignals. Where the reliability of the MEMS sensor at position A6 was found more reliablethan the other configurations since the optimal configuration for a higher speed wouldnot be reliable for a slow speed application.
A further discussions and conclusions to the research and the research questions arepresented in the following chapter 5.
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Chapter 5
Discussions and Conclusion
This chapter discusses the results of the research and forms a conclusion to both theresearch questions. A presentation is given to the overall significance of the coveredresearch and states the most important points of discoveries and also highlights thecentral issues of the research.
5.1 DiscussionThe study shows that the vibrations at slow speed applications poses challenges of de-tecting failure modes and thereby it is a challenge to implement a reliable conditionmonitoring method for slow speed applications. This proves the necessity of the re-search covered by this study and increases the significance of the results. However sinceslow speed applications of these pumps are less common to research the interest in theresearch field is mild.
The significance of the study is confirmed since the industrial recommendations areto not consider vibration analysis for slow speed applications under 100 rpm, where themethod has proved useful for slower speeds. It has been shown that the damaging failuremode cavitation can be detected during its development phase for slow speed applica-tions down to speeds of 30 rpm. Additionally the study shows the importance of choosingthe correct accelerometer type and position since there were only some accelerometerconfigurations viable for this application. As the study was only proved effective in alab environment on a specific pump with water, the study needs to be verified in a morerealistic scenario with more viscous fluids.
It could be argued that the energy is not a variable free from bias as it includes thesignal strength which is in favour for the MEMS accelerometer in the study, due to thesensor self resonance. Another way to compare the signals would be using the coherencyfunction which shows the resemblance of two signals compared to each other, presentinga value from zero to one. Another option is to simply calculate the difference betweentwo signals in terms of percentage, showing how much stronger one signal is comparedto the other. This could be done for the energy over the frequency spectrum where the
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difference shows present. However since the signal strength also is of interest the energywas deemed a better way to evaluate the performance.
Another argument could be that the peak value does not show the consistency of thesignal or the source of vibration. Where a RMS or average value would be better suitedin order to evaluate the consistency of the vibration. However other options than thepeak value does not exhibit the most severe vibrations where a peak value over a shorttime can give a better understanding of the severity of the vibrations from the cavitationin comparison to a RMS or average value over a time span. A better option would beto compare both RMS value and peak value for the time domain signal to better copewith the characteristic vibrations from cavitation.
The difference of peak characteristics and peak location in frequency spectra betweenthe two accelerometers was most likely due to the different functionality of the sensorsbut also since the MEMS accelerometer has a self resonance close to the frequency wherethe cavitation noise is present.
The amount of sensors in regard to reliability is another question to discuss whereadditional sensors increase the reliability of detecting cavitation and more clearly definingthe cavitation type and severity. If the sensors show the same condition it increasesthe reliability of the condition the sensors are showing. However it also introducesthe additional risk of one of the sensors failing and therefore resulting in uncertainty ofmeasurement if the result differ. The amount of sensors could help detect different failuremodes at different positions and for different speeds, thereby providing the measurementsto monitor additional failure modes and increase reliability of the system. If one of thetested positions is viable for monitoring of other failure modes the optimal position forcondition monitoring of all the failure modes might differ from the optimal position forcavitation detection as it would deem optimal in other ways of evaluation.
The study has validated the markers of cavitation both in time domain and frequencydomain independent on the type of accelerometer. Cavitation effects the over all level ofvibration where both the low frequency vibrations and the high frequency noise carriesa higher vibration force than normal operating condition, this can be seen in figures4.1, 4.2 and appendix D. It is also clear that the phenomenon of cavities imploding israndom in nature where the timing of these implosions can be seen in appendix C. Thehigh frequency noise which has high energy due to its peak characteristics where boththe peak location in frequency spectrum and peak characteristics differ depending onthe accelerometer position, this can be seen in appendix B.
5.1.1 Accelerometer configuration evaluationA discussion is given to the comparison of the accelerometer type, IEPE and the MEMSsensors with the performance of detecting the development of cavitation at variableslow speed applications for the bi-winged positive displacement pump. The differentpositions that was tested and evaluated by absolute difference in terms of energy fromfrequency domain and peak value from time domain of the noise signal, aswell as therelative difference in percentage of the energy of the frequency peaks. The configurations
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relation to the performance is discussed in terms of reliability.The accelerometer configuration has shown to be important in all of the aspects
included in the stated research question. The type of accelerometer in an implementationclearly results in different opportunities in terms of measurability. Likewise does theposition, where different positions results in varying signal strength. Where the bestposition is as close relation to the vibration source as possible. For cavitation in a rotarybi-winged positive displacement pumps, these positions are varying dependant on thetype of cavitation occurring in the pump. A reason for the delivery side to be the optimalposition for detection of cavitation development for slow speed applications could be dueto the internal back leakage in the pump, due to the pressure differences at the inletand outlet of the pump. Thereby detecting the development of cavitation before thephenomenon is fully developed.
This will likely give the most accurate measurements in order to as early as possibledetect the development of cavitation and estimate its effect to the seal lifetime.
The amount of accelerometer recommended are therefore discussed to be two for arotary bi-winged positive displacement pump. The first, in order to early detect the de-velopment of vortex cavitation at the suction side, to enable additional time of detectionin advance to a failure. The second, placed after the impellers on the delivery side, to beused for the identification of type of cavtation and the level of force. This configurationprovides the measurements to more accurately estimate the lifetime of a seal with pre-dictive maintenance. If more failure modes are to be monitored a combination of amountand position in regard to monitoring as many of the failure modes as possible needs tobe tested, where the optimal accelerometer configuration for cavitation detection mightnot even be a viable position for any other failure mode in slow speed applications. Thisalso allows to include one of each of the two sensor types in the evaluation increasingthe reliability of detecting the distinct character of the noise peak in frequency domain.
5.1.2 Cavitation detectionThe solution has proved to detect the state of initialization or development of cavitationfor slower speeds than what is generally used in the industry today. Where the configura-tion of a MEMS sensor placed in an horizontal axial position on the pump housing coverat the delivery side proved most effective at 30 rpm for the rotary bi-winged positivedisplacement pump.
Where only some of the tested configurations are reliable for detecting cavitationin a slow speed application of the bi-winged rotary positive displacement pump. Theseconfigurations were; positions T1, R1, I1 and A1 for the IEPE accelerometer and posi-tions A6 and A2 for the MEMS sensor. Other positions did not show sufficient differencebetween normal operating conditions and cavitation development.
Hence, the sub research question is discussed. There are similarities to the resultsof detecting cavitation with the use of accelerometers and simple signal processing foranother type of pump than in the compared studies. Even though the characteristicsof the peaks, energy of the signal and the frequency of the noise differs it shows thatthe method is applicable for slow speed applications of the rotary bi-winged positive
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displacement pump as well.Even if these initial cavitation conditions are not directly damaging to the pump,
it will lead to increased speeds for the application where it is intended to deliver aprecise amount of liquid over time. This will result in increased loads, increased amountof cavities formed and the development of stronger cavitation or even conversion ofcavitation type to a more directly damaging one. Thereby, also resulting in a lowerlifetime of not only the seals in the pump but also other internal components, which inturn relates to lowered machine reliability.
5.2 ConclusionRegarding the first research question of finding the optimal accelerometer configurationfor the tested use case a conclusion can be stated based on the results from the casestudy.
• What is the optimal configuration of accelerometers in terms of type and positionin regard to reliability when detecting cavitation in a slow speed application of arotary bi-winged positive displacement pump?[8][30][42]
The optimal configuration in the tested pump application out of the tested configu-rations was found to be the MEMS accelerometer in the horizontal axial position A6 onthe delivery side, located directly after the impellers on the pump house cover. This wasfound to be the optimal solution since the configuration gave the most clear distinctionbetween normal operating conditions and development of the failure mode cavitation.This is helpful for condition monitoring since it helps to detect the type of failure modeand thereby provides the means for a Machine Learning (ML) classifier to be trained onfuture collected historical data. Since the measurability proved effective even for slowerspeeds, the severity of cavitation to component lifetime can be estimated using the samehistorical data. Thereby showing a trend of vibration development where a ML regres-sion algorithm can help to predict the end of component lifetime. If cavitation can bedetected in an early stage and be either avoided or lowered it can help to increase thecomponent lifetime and thereby also the reliability of the machine.
The configuration can however not be directly installed in different applications orscenarios, as it still has to be validated or confirmed to each application. Since thepumps are constructed in different ways it results in different resonance frequencies forthe propagation of noise, thereby possibly changing the optimal solution to each appli-cation or version of the pump type.
The second research question has been answered and it has been shown that thereare clear similarities between the vibration noise that occurs from the cavitation for thetwo different pumps.
• What similarities of cavitation detection using vibration spectral analysis exist be-tween the accelerometer data from a gerotor pump and centrifugal pump comparedwith a rotary pump at slower speeds?
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Even though the signals are somewhat different in terms of both amplitude and fre-quency spectra, vibration analysis and FFT showed to be a good method for detectingcavitation for slow speed applications for the bi-winged positive displacement pump, inaddition to earlier research done to the gerotor and centrifugal pump. Following is apresentation of the results from two studies of comparison. The first study used for com-parison was "Diagnostic Process by Using Vibration Sensors for Monitoring CavitationPhenomena in a Gerotor Pump Used for Automotive Applications" [9]. The researchersof the study invoked cavitation with a 5mm and a 3mm orifice plate similarly as in thisthesis. The pump was operated at different speeds with different inlet pressure were theslowest speeds of 2000 rpm is used as a comparison to this study. The sensor in thisstudy was placed "near the cavitation zone" on the pump housing [9]. The results fromoperating the pump at 2000 rpm with the 3mm orifice plug can be seen in figure 5.1.
Figure 5.1: Three frequency spectra from first study of comparison, with a 3mm orifice,referenced as figure 8 in the results chapter in the compared study, collected from [9].
If compared with the results from the 50 rpm study in frequency spectra it, seenin appendix B it is noticeable that the peaks due to cavitation compared with normaloperating conditions show a clear indicator of the phenomenon. Even though the noisecharacteristics differ between the studies it shows that the noise are characteristic tosensor type, position and pump type. The lower the inlet pressure is and thereof also
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the bigger the difference between pressure at inlet and outlet the more intensive thecavitation develops, which can be seen by the energy in the graphs (as the area under thecurve). When comparing low cavitation or conditions close to no cavitation developmentit can be seen that the energy differs between the studies, in this thesis the cavitationdevelops as a small difference for a smaller spectrum, as can be seen in appendix A. Inthe compared study the cavitation noise can be seen in a broader spectrum showing theimportance of use case specific implementation of the sensors are needed, as can be seenin figure 5.2, where a 5mm orifice was used at a speed of 3000rpm.
Figure 5.2: Frequency spectra from first study of comparison, with a 5mm orifice,referenced as figure 5 in the results chapter in the compared study, collected from [9].
The second study used for comparison was "Monitoring of Mechanical Seals in ProcessPumps", where the researcher examines multiple failure modes on a centrifugal pump[8]. The test bench included eletronically controlled valves allowing the pressure at theinlet to be changed, invoking cavitation in the system. There was numerous tests tocavitation detection where it is stated that low pump speed does not fully develop thecavitation phenomenon and that the process is prolonged and takes significantly longerto fully develop caviataion.
Thereby it is harder to detect cavitation while in its development state. Followingis a presentation to test 33, where the centrifugal pump was operated at a speed of1500rpm and a negative inlet pressure [8].
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Figure 5.3: Frequency spectra from second study of comparison, for a speed of 1500rpm, referenced as figure 18 in the results chapter in the compared study, collected
from [8].
The peak characteristics vary between the thesis and the second study used forcomparison. The energy level shows the noise of cavitation present in high frequencyspectra and a peak at 10kHz can be seen in figure 5.3. This could be mistaken tobe due to self resonance, which is not the case since the sensor used in the comparedstudy was PCB - M352C67 which has a self resonance around 35kHz. The indicators forcavitation is present where it shows that a cavitation has not been fully developed sincethe over all vibration levels in this case was lower than for the BEP. However due to theenergy present in high frequency spectra it shows that the development of cavitation isoccurring. When comparing the noise for a higher speed of 3600rpm it is noticeable thatthe cavitation noise changes with the speed, as can be seen in figure 5.4, similarly as inthis thesis.
Figure 5.4: Frequency spectra from second study of comparison, for a speed of 3600rpm, referenced as figure 19 in the results chapter in the compared study, collected
from [8].
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It has been shown that vibration analysis with time domain and frequency domainusing simple signal processing techniques such as digital filtering and FFT proves to givevaluable information for condition monitoring of pumps, even for slower speeds than whatis generally recommended in the condition monitoring industry. The accelerometer posi-tion and type with its technical specifications of the sensor are important factors whichresults in an optimal accelerometer configuration, which is unique to the application andpump type.
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Chapter 6
Future recommendations
In this chapter the recommendations for further studies within the area is discussed.Additionally recommendations are given to the future work that should be implementedfor the project to be usable in the application.
6.1 Predictive maintenance developmentSince this work focuses on an optimal accelerometer configuration for detection of cavi-tation phenomenon in the system it is important to further develop the work in order forit to result in a usable application. Recommendations of development to work towardsimplementing predictive maintenance for the specific tested application was given ex-clusively to the stakeholder AkzoNobel, in a secondary report. However a presentationto how condition monitoring and predictive maintenance could be developed based onfurther development of the thesis work is presented.
For the work to be usable as condition monitoring of a positive displacement pump,some implementations are needed. Following since the raw data was manually recorded,continued work is needed for the monitoring to be continuous. In order to raise analarm, thresholds and triggers should be connected to these alarms. When the systemdetects a change in either RMS or peak value of the time domain vibrations, a FFTand spectral analysis could be done to verify cavitation as the failure mode causing thevibrations. These trigger levels should be implemented for the respective operationalspeeds where the change of peak value in time domain or RMS value shows a differencefor the vibrations and the high frequency spectra shows the noise from cavitation.
As the peak value from time domain and energy from frequency domain togetheroperate markers for classification of the condition a classifier algorithm could be de-veloped with further data recording. To allow for further development of a predictivemaintenance algorithm, data has to be stored over a longer period of time where a MLalgorithm could be trained on historical data. An evaluation of how often the datashould be stored are to be done in order to provide the means necessary to find trendsof vibration data and seal lifetime. A rough estimation of data recording to show thedevelopment of cavitation could be to store both the FFT result and RMS or peak val-
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ued raw data for 1-12 times per day. Thereby creating the opportunity to recognize thedevelopment of cavitation in the pumps, either over month, day or hour of operation.This allows for the development of a regression algorithm predicting lifetime of the sealdependant on the machines operational condition.
6.2 Future researchSince this research had many delimitations, it leaves many possible areas for futureresearch. Additionally, as this research project involve multiple subjects and researchareas, each of these has potential to be further investigated. The field of vibration anal-ysis of pumps has been around for a long time and has been vastly researched. Howeverthere still remains uncertainties in the research field as well as in the industrial applica-tions. Following the different fields included in the research is presented.
The research areas could be broken down to the following fields of research:
• Condition monitoring of pumps using sensor fusion
• Vibration analysis of pumps using low frequency accelerometers
• Signal processing of vibration data for slow speed applications
• Predictive maintenance and industrial optimization
The field of condition monitoring of pumps still has areas where new techniques andmethods should be investigated. One of the most interesting areas is sensor fusion forcondition monitoring of pumps. Allowing the combination of different sensor data fromdisparate sources, to reduce the uncertainty and result in a more accurate conditionmonitoring technique for pumps. This type of research is just briefly touched at thistime and needs to be investigated for different applications and different types of pumps.The combination of microphones and accelerometers to detect cavitation is an interest-ing combination which could prove more beneficial.
The field of vibration analysis of pumps has been around for a long time, but hasstill not resulted in optimal methods of analysis for different applications. Different or-ganizations use different directives, different methods and therefore should be further re-searched in order for find the best methods for the different failure modes. Since differentopinions are stated about the limits of vibration analysis for pumps it shows that the lim-its of measurability and vibration analysis for pumps has not been settled. Furthermorethe sensitivity of the accelerometer has a clear connection to the performance and mea-surability thereby posing possibility for better performance of measurablity with highersensitivity. It is also of interest to compare an IEPE and a MEMS accelerometer withthe same resonance frequency as it was found to be a contributing factor for cavitationdetection. Many recent research articles state high frequency bandwidth accelerometersto be the best suited sensor for cavitation detection since the noise of the implosions are
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visible in high frequency domain. However since low bandwidth accelerometers intendedfor low speed applications often have a self resonance within the domain of cavitationnoise, these accelerometers might result in better performance for cavitation detectionas the self resonance might act as a signal amplifier. Additionally these low bandwidthsensors often provide higher sensitivity, resulting in an area of interest for future research.
Signal processing of vibration data is another field in itself, posing many challengesto future research relating to conditions of pumps. Studies have been done with dif-ferent signal processing techniques. However these methods still needs to be evaluatedfor different application areas, where the slow speed applications poses challenges tomeasurability. FFT and vibration spectral analysis has been a standard for a long time,but with newer more advance signal processing methods it might not be the optimalsolution for condition monitoring. For example studies have been done using discretewavelet transforms in order to monitor the condition in pumps. Other areas within sig-nal processing relevant to condition monitoring are optimal filtering techniques, signalboosting techniques, windowing, smoothing functions and threshold value calculations.These are all relevant for condition monitoring of slow speed application pumps.
The last area somewhat involved in this thesis which is suggested for further re-search is predictive maintenance and industrial optimization. There are many availableoptions for predictive learners in terms of machine learning, deep learning or statisticalapproaches where different algorithms prove better for different use cases. Therefore itcould be of high interest to research the best predictive solution based on big data forcavitation detection.
A study within any of these areas could be done in connection to one or multiple ofthe other fields to connect the subjects more. This is heavily recommended since it isthe use case of condition monitoring or predictive maintenance which is the goal of thevibration analysis or signal processing of the data from the process pumps.
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Appendix A
Results from Case study at 30 rpm
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Appendix B
Results from Case study at 50 rpm
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Appendix C
Time domain data after HP-filter fromCase study at 30 rpm
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Appendix D
Time domain data after HP-filter fromCase study at 50 rpm
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