Detect Machinery Faults by Using Peak Vue

CSI RELIABILITY WEEK 1998

Melbourne, Australia

18 – 20 March 1998

“Using PeakVue to detect Machinery Faults”

Prepared and Presented by: Kris Goly

Senior Engineer – Predictive Maintenance Siemens Ltd

Technical Services Department

CSi Reliability Week Melbourne “Using PeakVue to detect Machinery faults” March, 1998

CONTENTS 1.0 Abstract 3 2.0 Introduction 3 3.0 PeakVue™ 4 4.0 Case Studies 9 5.0 Summary 13 6.0 Acknowledgments 13

Copyright@1998 Siemens Ltd - Rockhampton Page 2 of 13


1.0 Abstract This application paper describes a relatively new and not widely yet used vibration monitoring technique called PeakVue™. The technique was developed by CSi and is available as a standard feature on 2120 analysers. The paper will discuss the theory behind PeakVue™, its implementation and case histories.

2.0 Introduction Vibration analysis techniques have successfully been employed as a machine condition diagnostic and monitoring tool now for over forty years. At first it was the time trace analysis. Swept filters followed and with progress in electronics, spectrum analysis finally arrived. Roughly thirty years ago another technique was developed – High Frequency Acceleration Demodulation. However, it was only recently that this technique became widely available and is now a standard feature on many analysers/data collectors. With such a wide array of tools available and with growing experience the diagnosis of a machine condition was becoming more accurate. Analysts became more confident and more successful. With that came recognition from industry. In the 90’s industry has gone through a difficult time. Ever increasing competition, globalisation of the market and economy in crisis forced companies to look at their finances. It became apparent that the operation cost had to be reduced. One of the ways to achieve it was to decrease expenditure on maintenance. However, it had to be done without loss of equipment availability. Hence, industries ever increasing interest in Predictive/Proactive Maintenance. In such an environment the vibration analysts’ role has become more important and so has risen the expectations of good, infallible results delivered by vibration monitoring. Given the above no one vibration technique can be neglected and every new one should at least be trialled. Over the past 9 years Siemens Ltd has been providing Condition Monitoring Services to a variety of industries. Using standard data collection instruments and “off the shelf” software we have been able to successfully implement vibration programs which included machines with operational speeds between 50 and 6000 RPM. There was however a class of machines which was difficult to monitor and the success rate was less than satisfactory. These included all machines running below 50 RPM. The use of the acceleration Demodulation did help, although the technique was still not reliable enough and the fault detection rate was lower than expected. It was especially true with gearboxes as the filter set up was very critical on this type of equipment making it difficult to use Demodulation during routine monitoring. Another problem with Demodulation was the trendability or rather lack of it. Simply put, trending was not reliable enough to be meaningful. Since the development by CSi of PeakVue™, Siemens Ltd have been using it on a variety of equipment completely eliminating Demodulation. The technique has proven to be a very reliable tool for detection of antifriction bearings and gear problems on machinery running as slow as 20



RPM. As it will be shown in this paper the implementation and analysis process is very simple and does not require any special software or hardware.

3.0 PeakVue™

3.1 How it works? PeakVue™ is a new methodology of processing vibration signals. The vibration signal from an accelerometer is passed through a high or band pass filter and then the peak value of the time waveform is captured over a defined time interval (derived by Fmax of the spectrum and the number of waveform points). The methodology is also known as time or pulse stretching. By convention the data is plotted as one-sided waveform, with positive side only. After applying the FFT process a spectrum is obtained. The peak capture process is well suited for detecting high amplitude, short duration stress waves. The stress waves are mainly produced by faults where metal-to-metal contact occurs. Good examples of faults producing stress waves are antifriction bearing faults, gear defects and surprisingly electrical faults on motors. One may ask why PeakVue™ not Demodulation and what is the difference between PeakVue™ and Demodulation. The answer is obvious. PeakVue™ is:

• Trendable as it captures the true amplitude • Results do not depend much on filter settings • Can detect bearing defects on very slow machinery • Can detect defects with gears

For very slow speed machinery the Demodulation is not suitable, as it cannot detect signals of a very short duration. We found that while monitoring gearboxes the Demodulation process strongly depends on filter settings and can fail to detect obvious faults. This is not so with PeakVue™. 3.2 PeakVue™ implementation. In order to collect vibration data using PeakVue™ technology during routine vibration surveys a PeakVue™ measurement point has to be set up in a database. This process is similar to setting up a “normal” vibration point. The main difference is that the data has to be collected in acceleration units and a high pass filter has to be selected. A Master Trend™ screen showing an example measurement point is presented in Fig. 1. Note there is nothing extraordinary about the set up. The remainder of the set up is carried out in Analysis Parameter Set up. Before we start talking about Analysis Parameters set up just a short reminder what PeakVue™ detects – it detects stress waves associated with antifriction bearing faults and gear defects. Knowing this helps set up trend parameters and Fmax for spectrum.



The following should be kept in mind: • Fmax should be set up to capture faults as you normally would with routine

vibration readings • Cover approximately 5 harmonics of BPFI • Cover about 3.5 harmonics of gear mesh frequency • High pass filter has to be higher or equal to spectrum Fmax

Fig. 1 Measurement point set up in Master Trend™. For routine data collection Siemens Ltd have designed standard Analysis Parameters Sets as follows:

• Fmax = 50 orders • Resolution = 400 lines • Trendable parameters:

-overall level -5-50 orders -waveform peak

The high pass filters are set up at 500, 1000 or 2000 Hz depending on the application. For trending purposes the overall level and the waveform peak parameters are of the most value as they are the most meaningful and the fault limit levels are relatively easy to establish. Parameter 5-50 orders was set up at the time when we did not have much experience with PeakVue™. It is of not much value although as we do not want to “upset” the trend we left it unchanged.



Fig. 2 through Fig.4 shows a typical PeakVue™ AP Set up.

Fig. 2 PeakVue™ AP Set up –spectrum parameters.

Fig. 3 PeakVue™ AP Set up –signal processing parameters.



Fig.4 PeakVue™ AP Set up – trend parameters set up.

3.3 PeakVue™ - how to analyse data. For those accustom to spectrum analysis a PeakVue™ spectrum will look very familiar. All the usual fault frequencies will be the same. Fig. 6 shows a typical PeakVue™ spectrum with a bearing fault present. Things are a little different with time waveform. As it can be seen in Fig.7 the waveform is truncated or one sided and is similar to Demodulated waveform.



DR#3 - DRAG-LOW SPEED

DRAG#1 -I4D INTERM.SHAFT #2 DRUM END DEMOD

Route Spectrum 27-FEB-97 08:43 (PkVue-HP 1000 Hz)

OVRALL= 2.95 A-DG RMS = 2.96 LOAD = 100.0 RPM = 180. RPS = 2.99

0 40 80 120 160 200

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Frequency in Hz

RM

S A

ccel

erat

ion

in G

-s

Freq: Ordr: Spec:

18.50 6.183 1.289

Fig.6 A PeakVue™ spectrum. Bearing fault present.

DR#3 - DRAG-LOW SPEED


Waveform Display 27-FEB-97 08:43

RMS = 3.56 LOAD = 100.0 RPM = 180. RPS = 2.99

PK(+) = 22.44 PK(-) = 2.65 CRESTF= 6.58

0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3

-6

-3

0

3

6

9

12

15

18

21

24

Revolution Number

Acc

eler

atio

n in

G-s

Fig.7 A PeakVue™ waveform. Bearing fault present



4.0 Case Studies 4.1 Bearing inner race fault. We were monitoring a Toshiba 250kW 4 pole electric motor driving a mine ventilation fan through a single stage reduction gearbox. In September 1997 there were some changes indicating a bearing inner race fault. Fig.8 shows both spectrum and time waveform data. Note all the peaks in the spectrum are of low amplitude. One would be hard pressed to find the BPFI frequencies as almost all peaks are synchronous. More information is in the time waveform. Combining both of them one could draw conclusion that an inner race fault is developing.

Fig.8 Spectrum and time waveform of an inner race fault developing.

Fig.9 PeakVue™ spectrum and waveform. An inner race fault clearly visible.

As the motor is critical for the operation of the plant during our next vibration survey we have collected also PeakVue™ data. It enabled us to correctly identify the fault. Fig. 9 contains PeakVue™ data. An inner race fault is clearly visible. The time waveform provided additional information on severity of the fault. Following our recommendation the customer replaced the motor during the Christmas break. The motor was sent for overhaul and the bearing was inspected. A crack in the inner race was found. One could argue that the fault could have been detected using “conventional” methods, however by applying PeakVue™, analysis was easier and more accurate.


WAVEFORM DISPLAY 23-SEP-97 11:26 RMS = .5769 PK(+) = 2.43 PK(-) = 2.39 CRESTF= 4.21

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-3

-2

-1

0

1

2

Revolution Number

Acc

eler

atio

n in

G-s

020 - MOTOR

FAN # 2 -M1H MOTOR NDE BEARING

ROUTE SPECTRUM 23-SEP-97 11:26 OVRALL= .5956 A-DG RMS = .5957 LOAD = 100.0 RPM = 1485. RPS = 24.75

0 20 40 60 80 100

0

0.06

0.12

0.18

0.24

Frequency in Order

RM

S A

cc in

G-s

Ordr: Freq: Spec:

4.907 121.46 .00554

E E E E E E E E E E E E

WAVEFORM DISPLAY 09-DEC-97 17:21 RMS = 1.78 PK(+) = 9.74 PK(-) = 3.30 CRESTF= 5.48

0 2 4 6 8 10

-4

0

4

8

12

Revolution Number

Acc

eler

atio

n in

G-s

020 - MOTOR

FAN # 2 -M1H MOTOR NDE BEARING

ANALYZE SPECTRUM 09-DEC-97 17:21 (PkVue-HP 1000 Hz) RMS = 1.54 LOAD = 100.0 RPM = 1485. RPS = 24.75

0 5 10 15 20 25 30 35 40 45

00.10.20.30.40.50.6

Frequency in Order

RM

S A

cc in

G-s

E

Ordr: Freq: Spec:

4.902 121.32 .469

E E E E E E E



4.2 Bearing fault on a variable speed gearbox-low speed. Next example is from a variable speed gearbox. For some time now Siemens Ltd has been involved in vibration monitoring of Draglines. Draglines are machines used to remove overburden in open cut coal mines (sorry for explaining obvious). This case study is from a drag gearbox intermediate shaft. The running speed of the shaft changes in a matter of seconds from stationary to around 180 RPM. Usually vibration data is collected at 120-160RPM. Fig.10 shows velocity spectrum. As it can be seen no bearing faults are visible. The peaks marked by cursor are gearmesh frequencies. For this particular application the amplitude is not regarded to be excessive. Fig.11 shows PeakVue™ data. The bearing defect is obvious. Based on the rate the trend was increasing the bearing was replaced. Fig.12 represents a PeakVue™ trend and Fig.13 comparison spectra (faulty and new bearing). Note the significant change once the bearing was replaced. Upon inspection the outer race was found to have significant spalling and pitting in the load zone.

Fig.10 Velocity spectrum

Fig.11 PeakVue™ spectrum and waveform. Bearing defect well defined

Fig.12 PeakVue™ trend of a bearing fault.

Fig.13 Comparison spectra: faulty and a new bearing.

010 - DRAG-LOW SPEED

DRAG#1 -I4 INTERM.SHAFT #2 DRUM END

Analyze Spectrum 27-FEB-97 08:42

RMS = 11.47 LOAD = 100.0 RPM = 100. RPS = 1.66

0 10 20 30 40 50 60 70

0

2

4

6

8

10

Frequency in Order

RM

S V

eloc

ity in

mm

/Sec

Ordr: Freq: Spec:

3.014 5.012 .298

WAVEFORM DISPLAY 27-FEB-97 08:43 RMS = 3.41 PK(+) = 22.44 PK(-) = 2.65 CRESTF= 6.58

0 1 2 3 4 5 6 7

-6

0

6

12

18

Revolution Number

Acc

eler

atio

n in

G-s



ROUTE SPECTRUM 27-FEB-97 08:43 (PkVue-HP 1000 Hz) OVRALL= 2.95 A-DG RMS = 2.94 LOAD = 100.0 RPM = 180. RPS = 2.99 0 10 20 30 40 50 60 70

0

0.4

0.8

1.2

1.6

Frequency in Order

RM

S A

cc in

G-s

Ordr: Freq: Spec:

6.183 18.50 1.273

C C C C C C C C C C



Trend Display of OVERALL VALUE

-- Baseline -- Value: .258 Date: 25-JUL-95

0 200 400 600 800 1000

0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

Days: 25-JUL-95 To 23-OCT-97

RM

S A

ccel

erat

ion

in G

-s

Date: Time: Ampl:

23-OCT-97 08:10:33 .139

BEARING FAULT

NEW BEARING

RM

S A

ccel

erat

ion

in G

-s

Frequency in Order



0 10 20 30 40 50 60 70

Max Amp 1.28

Plot Scale

0

0.7

27-FEB-97 08:45

09-APR-97 11:21

Ordr: Freq: Sp 1:

6.230 18.65 1.281

FAULTY BEARING

NEW BEARING



4.3 Bearing fault on a variable speed gearbox-moderate speed. Next example shows how valuable PeakVue™ is for “once off” vibration snap shots. Some time ago Siemens Ltd received a phone call from one of our customers asking if we could help them identify a problem they had with one of their Draglines. A conversation with the maintenance superintendent revealed their cause of concern: there was a “knock” in one of the hoist gearbox bearings. It was occurring only during change of direction of rotation. A bearing cage defect was suspected. “Normal” vibration data collected on this particular bearing did not detect any faults. As it can be seen in Fig.14 there are no peaks in the spectrum, which would indicate a bearing fault. Time waveform is considered to be normal for this particular application. Things look different when it comes to PeakVue™ data (refer Fig.15). Spectrum contains BPFO harmonics only so the diagnosis was simple. Waveform data provides further information on fault severity.

Fig.14 Velocity spectrum. No bearing faults visible.

Fig.15 PeakVue™ spectrum and waveform. Bearing fault clearly visible.

In this particular case the customer was very surprised to hear that there was a severe outer race fault, not a cage defect. The bearing was replaced during the next scheduled maintenance window. As predicted severe spalling and scoring was present in the load zone of the outer race. This case study proves that even if there is no historical data it is still possible, using PeakVue™ technology, to detect bearing faults and assess its severity.

WAVEFORM DISPLAY 28-OCT-97 12:28 RMS = .9547 PK(+) = 2.41 PK(-) = 3.72 CRESTF= 3.90

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-5

-3

-1

1

3

Revolution Number

Acc

eler

atio

n in

G-s

010 - HOIST-MEDIUM SPEED

HOIST#1 -G2 SHAFT #1 DRUM END

ROUTE SPECTRUM 28-OCT-97 12:28 OVRALL= .9581 A-DG RMS = 4.22 LOAD = 100.0 RPM = 1004. RPS = 16.74

0 10 20 30 40 50 60

00.20.40.60.81.01.2

Frequency in Order

RM

S V

el in

mm

/Sec

Ordr: Freq: Spec:

33.12 554.28 .681

E

WAVEFORM DISPLAY 28-OCT-97 12:29 RMS = 3.37 PK(+) = 21.03 PK(-) = 2.32 CRESTF= 6.24

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-6

0

6

12

18

Revolution Number

Acc

eler

atio

n in

G-s

010 - HOIST-MEDIUM SPEED

HOIST#1 -G2D SHAFT #1 DRUM END DEMOD

ROUTE SPECTRUM 28-OCT-97 12:29 (PkVue-HP 2000 Hz) OVRALL= 3.50 A-DG RMS = 3.46 LOAD = 100.0 RPM = 738. RPS = 12.30 0 20 40 60 80 100

0

0.6

1.2

1.8

2.4

Frequency in Order

RM

S A

cc in

G-s

Ordr: Freq: Spec:

9.752 120.00 2.016

C C C C C C C C


4.4 Conveyor pulley-case#1. Until now all the case studies presented were from relatively fast running equipment. In most of these cases the Demodulation technique would have been probably good enough to detect a bearing fault although it would not provide a quantitative measure of the fault severity. The following case history is from what is considered to be very slow running machinery – a conveyor pulley running at 33RPM. This particular pulley was causing some problems in the past. Prediction of bearing faults was difficult. As this is a conveyor drive drum, vibrations from the gearbox were transmitted through the shaft to the bearing housing effectively masking any bearing faults, which could have been present. The Demodulation technique employed did help, however the results were still far from satisfactory. As a result of such experience with this particular pulley Siemens Ltd has decided to try PeakVue™. First we noticed a considerable improvement in data quality. Trends became “trendable” and reliable. Fig.16 shows a vibration overall trend recorded on this particular bearing. Note the change after a new bearing was installed. Fig.17 is a comparison spectra of a new and a faulty bearing.

Fig.16 Trend of rising bearing fault. Fig.17 Comparison spectra: top-new bearing; bottom-outer race fault.

As it can be seen from the above even with such slow running equipment and a “noisy” environment it is possible to successfully detect and predict a bearing failure using the PeakVue™ technology. 4.5 Conveyor pulley-case#2. This case study also comes from a conveyor pulley. The shaft is rotating at 23RPM. A PeakVue™ MP waveform trend is shown in Fig.18. Again, the trend clearly indicates bearing deterioration. A spectrum corresponding to the maximum value of trend is shown in Fig.19. Note the significant difference in trend once the bearing was replaced. The bearing was inspected after replacement. As expected the outer race was damaged in the load zone.

020 - CONVEYOR PULLEY

RC1M -1LD DRIVE DRUM LHS BRG HORZ 766

Trend Display of OVERALL VALUE

0 50 100 150 200 250 300 350 400

0

0.004

0.008

0.012

0.016

Days: 02-DEC-96 To 28-NOV-97

RM

S A

ccel

erat

ion

in G

-s

Date: Time: Ampl:

28-NOV-97 12:58:22 .00340

RM

S A

ccel

erat

ion

in G

-s

Frequency in Order

020 - CONVEYOR PULLEYRC1M -1LD DRIVE DRUM LHS BRG HORZ 766

0 20 40 60 80 100

Max Amp .0043

PlotScale

0

0.003

07-OCT-97 11:10

28-NOV-97 12:58

Ordr:Freq:Sp 1:

9.758 5.399 .00394

NEW BEARING

FAULTY BEARING




Fig.18 PeakVue™ Peak Waveform trend of a bearing outer race fault.

Fig.19 PeakVue™ Spectrum recorded on a bearing running at 23RPM. Outer race fault present.

5.0 Summary Introduced 2 years ago a new methodology, PeakVue™, of vibration signal processing has proven to be an invaluable tool for prediction of antifriction bearing faults and gear problems. Its application is simple and does not require special software or hardware. A standard CSi 2120 machinery analyser and Master Trend™ software is all that is needed. For medium and high speed machinery PeakVue™ is similar to Demodulation. For low speed it gains an advantage as it can detect stress waves usually missed by Demodulation. PeakVue™ data is trendable and as such is more suitable for routine condition monitoring. The time waveform provides true amplitude making assessment of fault severity possible.

6.0 Acknowledgments I like to express my gratitude to all Rockhampton CM Team Members whose sweat paved the road during collection of the vibration data used in this paper.

020 - REJECT CONVEYOR RR1M ROM#1

RR1M -6RD RETURN (TAIL) PULLEY RHS BRG 766

Trend Display of PEAK WAVEFORM

0 100 200 300 400 500

0

0.04

0.08

0.12

0.16

0.20

Days: 02-DEC-96 To 15-JAN-98

RM

S A

ccel

erat

ion

in G

-s

Date: Time: Ampl:

15-JAN-98 17:08:26 .02307

020 - REJECT CONVEYOR RR1M ROM#1

RR1M -6RD RETURN (TAIL) PULLEY RHS BRG 766

ROUTE SPECTRUM 29-AUG-97 10:24 (PkVue-HP 1000 Hz)

OVRALL= .0068 A-DG RMS = .0075 LOAD = 100.0 RPM = 23. RPS = .38

0 10 20 30 40 50 60

0

0.0006

0.0012

0.0018

0.0024

0.0030

Frequency in Order

RM

S A

ccel

erat

ion

in G

-s

Ordr: Freq: Spec:

7.730 2.929 .00155

>SKF 22213C J=BPFO : 7.73

J J J J J J

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Detect Machinery Faults by Using Peak Vue