Vib - The Capture and Analysis

7/30/2019 Vib - The Capture and Analysis

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DoctorKnow Application PaperTitle: The Capture and Analysis of Stress Waves Provides SignificantImprovement inCondition Monitoring of Critical Rotating Machinery

Source/Author:David M. Stobbe, Marc Phillips, James Robinson

Product: PeakVue

Technology: Vibration

The Capture and Analysis of Stress WavesProvides Significant Improvement in

Condition Monitoring of Critical Rotating Machinery

by David M. Stobb e, National Steel Corp ., Great Lakes Divisio n - Michig an;

Marc Phi l l ips and James Robinso n, Computat ional

Systems, Incorpo rated - K nox vi l le , Tennessee

BIOGRAPHIESDavid M. Stobbe is Reliability Engineer, Maintenance Technology Group of National SteelCorp., Great Lakes Division. He is a Certified Plant Engineer and Certified VibrationSpecialist Levels II and III. David has been active in the general machinery conditionmonitoring as a vibration analysis specialists, balancing, alignment, and root causeidentification since 1984. David has a BS degree in Mechanical Engineering.

Marc Phillips is a Senior Engineering Consultant with CSI Services in Knoxville, Tennessee.Marc has spent 7 years in the field of Condition Monitoring and Vibration Analysis and hasBS and MS degrees in Mechanical Engineering.

James C. Robinson is Senior Technical Advisor in the Engineering Group for ComputationalSystems, Incorporated in Knoxville, Tennessee. Prior to joining CSI, he was with theTechnology for Energy Corporation which he co-founded. He has had 20 plus yearsexperience in the design and development of vibration monitoring equipment used for

condition monitoring. Prior to TEC, he was on the Nuclear Engineering staff of theUniversity of Tennessee for 15 years.

ACKNOWLEDGMENTThe authors express their appreciation to the many insights provided by Joe Dominick ofWeirton Steel on the importance of peak value stress wave analysis to rotating machineryproblems typically encountered in the steel industry. Joe, formerly with CSI Services,provided the case study on the bridle gear box with the cracked teeth.

ABSTRACTStress waves accompany metal-to-metal impacting, fatigue cracking, scuffing, abrasive

wear, and other defects commonly encountered with rotating machinery faults. These stresswaves are short-term term (fractional to a few milliseconds) transient events whichintroduce ripple on the surface of machinery as they propagate away from the initiatingevent. The important features to capture for fault diagnostics is the magnitude of the events


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and rate (periodic or random) of occurrence. In this study, the stress waves are captured bya) separating them from the normal vibrations through the use of high pass filters, b)capturing the magnitude through recording the peak values (identified as PeakVuemethodology) over repeated continuous small time increments consistent with the analysisbandwidth, and c) identifying periodicity through spectral analysis. The analysismethodology has been implemented into a standard data collector/analyzer employed inroutine condition monitoring of rotating machinery based on vibration analysis.

The PeakVue methodology has proven to be a very valuable tool to add to the analyst toolbox. Representative results showing bearing faults and cracked gear teeth are presented.The faults demonstrated are:

1. Outer race bearing defect in a critical pinion stand gear box on a tandemmill.2. Cracked gear teeth on output shaft of a double speed reduction precisiontension bridle gear box.3. Outer race bearing defect on a mandrel drive motor.4. Outer race bearing defect on a centrifugal water pump.

5. Inner race bearing defect on a Basic Oxygen Furnace (BOF) vessel.

The conclusion from this study is the PeakVue methodology significantly improves theanalyst capability to detect and classify severity of bearing and gearing defects commonlyexperienced in the steel industry.

1.0 INTRODUCTION

Representative results from vibration analysis employing standard vibration and stresswave analysis for two gear boxes, a motor, a pump, and a basic oxygen furnace (BOF)vessel are presented. The faults detected and presented are from defective bearings and

cracked teeth in a gear box. The standard vibration analysis employ the velocity spectraldata methodology. The stress wave analysis employ the peak value (PeakVue 7th)methodology implemented on a standard portable data collector/analyzer.

For very slow speed machinery such as the BOF vessel and gear boxes, the stress waveanalysis employing PeakVue are demonstrated to be a valuable tool for the purposes of a)detecting faults otherwise missed with standard velocity spectral analysis, and b) assist inestablishing the severity level.

In the next section, a brief discussion of stress waves and reasons for the appearance inrotating equipment are presented. This will be followed by a brief discussion of the analysis

methodology employed in PeakVue. Representative case studies from two gear boxes, amotor, a pump, and a BOF vessel are presented in Section 3. The conclusions drawn fromthis study are presented in the final section.

2.0 STRESS WAVE ACTIVITY AND ANALYSIS METHODOLOGY

2.1 Stress Wave Activity in Metallic SystemsStress waves are set up in metallic system when events such as impacting, fatiguecracking, scuffing, abrasive wear, etc., occur. Stress waves in metal appear as bothlongitudinal and bending waves. Bending waves introduce ripples on the surface (henceexcites an accelerometer attached to surface) as they propagate away from the initiatingsite at the speed of sound. The stress waves are of short duration (fractional to a fewmilliseconds) and hence appear in the output of an accelerometer as short-term transientevents.


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2.2 Analysis MethodologyThe velocity spectral analysis was carried out using standard guidelines for selectinganalysis bandwidth, resolution, etc. Stress waves, which are short-term transient events,are characterized by broad frequency content. This makes possible the separation of thestress waves from the normal vibration through the use of high pass filters. For gearing

systems, the general rule for selecting the high pass filter is greater than three times gearmesh since excessive wear on teeth will often manifest itself at three times gear mesh. Forother systems, the high pass filter is typically selected at 30 to 50 times shaft speed (greaterthan 3 to 5 harmonics of inner race bearing defect).

The spectral analysis bandwidth for PeakVue are selected following the same general rulesas used in normal spectral analysis. The selection of the spectral analysis bandwidth,Fmax, defines the sampling rate to be employed (the sampling rate typically is set at 2.56times Fmax).

In normal spectral analysis, the signal is routed through a high order, low pass (anti-aliasing) filter to remove most energy associated with frequencies greater than one half thesampling rate (avoids aliasing). The analog signal which emerges from the anti-aliasingfilter is converted to a digital representation by sampling the analog signal at the discretetime intervals established by the analysis bandwidth. Each digital representation is theamplitude of the signal emerging from the anti-alaising filter at the instant in time the analogsignal was digitized. The anti-aliasing filter removed any significant variation of signal(possibly short-term transient event) which could have occurred between sample times.Hence normal spectral analysis generally are not responsive to stress wave activity.

Stress waves accompany many mechanical faults found in rotating machinery. The specific

frequencies found within stress waves generally are not of interest. What is most useful isthe detection and quantification of the events relative to a) rate of occurrence, and b)magnitude of individual events. In PeakVue, the detection and specification of magnitude ofindividual events and identification of rate of occurrence are accomplished by:

1. Separate stress waves from normal vibration with high pass filter.2. Find absolute peak value of signal over each time increment specified byanalysis bandwidth. The block of data acquired for further processing, e.g.,FFT, consists of peak values for each increment of time in lieu ofinstantaneous values used for normal analysis.3. Perform spectral analysis, FFT, for the block of peak values which willidentify any periodic event rate. Random event rates will increase the spectral

noise floor. The PeakVue time waveform consists of the peak value block ofdata used for FFT analysis.

3.0 CASE STUDIES3.1 IntroductionFive cases have been selected for presentation for illustrating the importance of peak valuestress wave analysis and normal spectral vibration analysis for the detection andspecification of severity level of faults found in machinery in the steel-making industry. Thecases consists of a gear box with defective bearing, a gearbox with cracked teeth, a motorwith defective bearing, a pump with defective bearing, and a BOF vessel with defectivebearing.

For some of these cases, the fault was very obvious from the peak value stress wave


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analysis but not detectable in the normal spectral analysis. For one case, the severity of thefaults was much more obvious in normal spectral analysis than in the peak value stresswave analysis.

3.2 Pinion Stand Gear BoxThis gear box is carefully monitored (total of 16 points) on a monthly basis for earlydetection of bearing/gearing problems. On the outlet (mill) end of the input shaft, possible

inner race bearing defect begin to show in the vibration velocity spectral data at a very lowlevel in October 1996. The outer race defect begin to show in the velocity spectral data inAugust 1997 at even a lower level (0.008 ips). The inner and outer race defects detected inthe normal velocity spectral data showed no upper trend prior to bearing replacement onOctober 22, 1997. Since the levels were small and no upper trend was present, the severitylevel was judged to be minimal based on velocity spectral data alone.

Stress wave analysis (PeakVue) was employed in July 1997 for the bearing of concern(input shaft on mill end). There clearly were significant impacts occurring (14g's) at theouter race defect frequency. By September 1997, the impact levels had increased to

around 27g's. The PeakVue spectra and time wave form for data acquired in August 1997are presented in Figure 1. The high g-levels observed in PeakVue raised concern relative tothe severity level of the fault on this bearing. This led to replacing the bearing on October22, 1997. A picture of the outer raceway showing etching (water moisture damage) ispresented in Figure 2.

Figure 1. PeakVue data from pinion stand gear box on inlet shaft, mill end on August

18, 1997.

Figure 2. Picture of bearing showing faults from pinion stand gearbox, inlet shaft,mill end - bearing removed on October 22, 1997.


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The stress wave analysis, PeakVue, on this bearing a) identified clearly there was a defectof concern on the outer raceway, and b) the severity level was sufficient to warrant achange out. The normal velocity spectral data never clearly demonstrated a fault andcertainly not a fault of any concern due to a) very low level, and b) lack of any upper trendin level.3.3 Tension Bridle Gear Box

A plan view of the precision bridle gear box is presented in Figure 3. There are two outputshafts where the second is driven by a 90 tooth gear vertically above one shown. The onlypoint accessible for measurement was on the input shaft bearing.

Figure 3. Plan view of bridle gear box. Second output shaft directly above oneshown. Driven by 90 tooth gear to 90 tooth gear set.

Typical velocity vibration spectra taken on this gear box on April 11, 1997, are presented inFigure 4. The input gear mesh at order 40 has "skirts" which were on the increase and hadinitiated a recommendation that the input gear set be inspected for accelerated wear atsome time in the future (not a high priority).

Figure 4. Velocity vibration spectra from bridle gear box taken on April 11, 1997.

On April 14, 1997, a PeakVue data set was acquired from this gear box. The PeakVue


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spectral and time wave form data are presented in Figure 5. The PeakVue spectral datahas a large peak at the output shaft turning speed and two significant impacts (35 to 40 g's)per turn of the output shaft. This suggests strongly that one of the gears on the output shafthas two regions where there are either cracked or weak teeth. A broadband (5,000 Hz)PeakVue spectra are presented in Figure 6. The "ski slope" activity is due to the fact thatmost activity is occurring at the turning speed of the output shaft (3.6 Hz) which is below theresolution of this analysis. The important information here is there are gear mesh activity

occurring at the 90 tooth gear mesh (none at the 158 tooth bull gear mesh).

Figure 5. PeakVue data from bridle gear box taken on April 14, 1997.

Figure 6. 5000 Hz bandwidth PeakVue spectral data from bridle gear box in horizontaldirection on April 14, 1997.

The impacts, probably cracked teeth, could be coming from the 90 teeth gear set or the 158tooth bull gear located on the output shaft. The velocity spectral data, see Figure 4,suggests that the problem may be with the bull gear since this is the gear mesh associatedwith the 40 tooth/158 tooth gear set. The PeakVue data set presented in Figure 5 did nothave sufficient bandwidth to encompass the gear mesh frequencies; however, a 5000 Hzbandwidth PeakVue data set was acquired in the horizontal measurement set. ThePeakVue time waveform and spectra identified the cracked teeth problem and located it tobe on one of the two 90 tooth gears on the output shafts.


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Following acquisition of the PeakVue spectral data, it was recommended that the outputgear set be replaced as soon as possible. The gear set was replaced over the weekendand two teeth from the 90 tooth gear had visible cracks developing around the roots. Theinput set was found to be worn and showed signs of misalignment.

Velocity spectral data alone would not have identified the cracked teeth. The PeakVue timewaveform and spectra identified the cracked teeth problem and located it to be on one of

the three gears on the output shafts. The broadband PeakVue spectral data then furtherreduced the probable gear set to be the output gear set.

3.4 Mandrel Drive MotorThis Mandrel drive motor was installed December 12, 1997. Abnormal noise was reportedon March 12, 1998. Daily vibration measurements were initiated. The velocity spectral dataacquired on March 16, March 17, at 10:00 a.m., and March 17 at 1:17 p.m. are shown inFigure 7. The corresponding time waveforms are presented in Figure 8. Clearly an outerrace defect (8.3 orders) is present and increasing rapidly.

Figure 7. Velocity vibration spectra from a Mandrel drive motor over a two-day periodof March 17, 1998. Outer race defect activity highlighted.


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Figure 8. Time waveform corresponding to the velocity spectra data presented in

Figure 7.

The PeakVue data (spectra and time waveform) taken at the same measurement point onMarch 17th at 1:11 p.m. are presented in Figure 9. The outer race defect is present here,but the time waveform does not show levels to express the same sense of urgency as thetrend data in normal spectral analysis. PeakVue data was acquired only the one time.

Figure 9. PeakVue data from Mandrel drive motor acquired on March 17, 1998. Bothouter race and ball spin faults are present.

Figure 10. Picture of outer race from Mandrel drive motor bearing showing faults.

The rapid trend in the spectral data initiated a priority work order to replace the bearing. The

bearing was replaced on March 25th. A picture of the damaged bearing is presented inFigure 10. Approximately 60% of the outer-ring lip had been fractured.

The PeakVue data supported the conclusion from the normal vibration analysis that therewas an outer race defect. The severity of the defect was detected from the trend dataassociated with the normal spectral data. Trend data was not available from PeakVue. Thiscase study illustrates the importance of acquiring both spectral and PeakVue data.

3.5 Service Water PumpA centrifugal service water pump rated for 12,000 gpm is driven by an 8-pole (710 RPM)700 horsepower motor. Routine spectral analysis was showing bearing faults at a

reasonably low level. Severity was ranked at alert level. Velocity spectra data with outerrace highlighted for data acquired on December 8, 1997, are presented in Figure 11.PeakVue spectra acquired on December 8 showed very strong, about 10 g's, impacting atthe outer race defect frequency. The measurements were repeated on December 22. The


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velocity spectral data at the outer race defect frequency had increased by 50% relative tothe level shown in Figure 11. The PeakVue data acquired on December 22 are presented inFigure 12. The impacting levels had increased to 30+ g's (threefold). This significantincrease in the impacting levels moved the severity to FAULT verses ALERT. The bearingwas removed and deep spalls found on inner and outer races (see Figure 13).

Figure 11. Velocity spectra from service water pump for data acquiredon December 8, 1997. Outer race fault frequency highlighted.

Figure 12. PeakVue data from service water pump for data acquiredon December 22, 1997. Outer race fault frequency highlighted.


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Figure 13. Picture of outer and inner race for defectivebearing from the service water pump.

The bearing faults were identified in the normal velocity spectral data but judged notserious. The PeakVue analysis raised the level of concern due to the large levels of theimpacting events. This led to replacement of the defective bearing in a scheduled manner.

3.6 Basic Oxygen Furnace (BOF) Vessel

The basic oxygen furnace vessel, see Figure 14, is one of several very slow speedmachines in the steel industry which are critical to operations. These generally are verylarge machines with, e.g. bearings that are large, expensive, long lead times, etc. Advancedwarning of impending failure of specific components becomes very useful informationrelative to scheduling repairs, etc.


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Figure 14. Basic oxygen furnace (BOF) vessel and associated drive.

In machinery rotating at speeds in excess of 300 RPM, normal velocity spectral analysishave proven to be an effective tool for early detection of commonly occurring faults such asbearing defects. Additionally, the incorporation of stress wave analysis has also proven tobe helpful for these class of machines and can provide capability of detecting other faultssuch as cracked teeth in gear boxes, etc. For very slow machinery (less than 10 RPM),normal velocity spectral analysis provides little or no reliable detection capability. On theslow speed machinery, stress wave analysis provides the only reliable detectionmethodology.

Stress waves are transient events characterized by reasonably high frequency (1 to 10

kHz). To reliably capture these events in a digital manner, the time domain data must besampled at a high rate (order of 50,000 samples per second). For a vessel like the BOFvessel which is turning at 0.5 RPM, very large data blocks would be required to store data(one complete revolution would require 12 megabytes). The BOF only turns about 160,hence only 3 megabytes are required for data storage for one swing.

Once the data is captured, the analyst has the task of "analysis". The use of spectralanalysis is not an option here. What has been done is to capture the data (usually at a lessthan desirable sampling rate such as 12K samples per second) and store in a spectrum ortransient capture instrument. Then the data can be displayed as a "compressed time trace"where the entire block of data is displayed on a single screen where only max and min

values within the horizontal resolution are displayed. The number of points over which themax/min values are selected is simply determined by dividing the total number of datapoints by the horizontal pixel resolution, e.g., if we have 106 data points and 103 pixels, the


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max/min values are selected from 103 data points for each pixel.

An example of capturing a large block of data and displaying a compressed time waveformfrom a sensor on the bearing housing of a BOF vessel is presented in Figure 15. This datawas acquired at a sampling rate of 2.56 time 5KHz or 12.8 K samples/sec (which is about1/4th the generally accepted required sampling rate). In this data, there clearly areimpacting events occurring at some reasonably repetitive rate. The task for the analyst then

is to determine the most probable source for these events and then estimate severity. Forthis task, it would be helpful to have historical data to compare with, trend, etc. The largeblocks of data from which this compressed time waveform was extracted makes trendingdifficult due to large storage requirements, etc.

Figure 15. Compressed time trace 5 KHz bandwidth block for 45 secondblock of data from accelerometer located on BOF bearing.

At a certain facility where two BOF vessels are present, the vessels were turned throughtwo complete turns (at nominal rate of 0.5 RPM) and PeakVue time waveform captured.

One of the two vessels has indication of a defective inner race. The time waveform coveringthe two revolutions are presented in Figure 16a. The portion of the PeakVue time waveformthrough the defective regions are expanded and presented in Figure 16b. Here the timebetween impacts which would indicate inner race are highlighted. The conclusion is thatthere is an inner race defect which has initiated a) trending, and b) scheduling replacement.

In PeakVue as currently implemented, a sampling rate of 100,000 samples per second isused to find the peak values. For a specified analysis bandwidth of 20Hz and 1600 lines,the PeakVue time waveform is 4096 data points for 1600/20 or 80 seconds of elapsed time.This clearly encompasses one work swing of the BOF vessel. Since we are dealing withonly 4x103 data points in PeakVue versus 4 x 106 data points required for the raw data,

trending, order tracking, etc., becomes a very practical capability.


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Figure 16a. PeakVue time waveform from accelerometer mounted on BOF vesselbearing.

Figure 16b. Expanded PeakVue time waveform through active region.

Figure 16. PeakVue time waveform from accelerometer mounted on BOF vesselbearing.

4.0 SUMMARY AND CONCLUSIONS

Emphasis has been placed in this paper on the analysis of stress waves employing thepeak value (PeakVue) analysis methodology for detecting and classifying severity level of


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faults typically experienced in machinery commonly encountered in the steel industry. Thespeed range for the machinery monitored for this study ranged from 0.5 to 900 RPM. Bothstress wave and normal vibration analysis were employed for machinery greater than 100RPM. Only stress wave analysis was considered for machinery less than 10 PRM.

The implementation of the PeakVue stress wave analysis was accomplished using thesame instrumentation as was employed for the normal vibration analysis and required

minimal effort (no additional hardware or software) from the operator. The primary source ofinformation from normal velocity spectral analysis is the velocity spectral data. In thePeakVue data analysis, both the spectral and peak value time waveform data are importantfor fault identification and severity evaluation.

For the faults such as cracked teeth in gear boxes and bearing faults on the BOF vessel(0.5 RPM), the PeakVue analysis was the only methodology which permitted detection andseverity evaluation. For other faults, both PeakVue and normal velocity spectral analysiscontributed to detection and severity evaluation. In some cases, it was PeakVue which wasparamount in initiating corrective action while in others it was normal velocity spectral

analysis which initiated the corrective action.

The conclusions are:1. The condition monitoring program should continue with thenormal velocity spectral data collection, trending, alarming, etc.2. The inclusion of stress wave analysis employing amethodology which provides both spectral data and, equallyimportant, maintain the true peak values in the time domainshould be a part of the standard condition monitoring program.

All contents copyright 1998, Computational Systems, Inc.All Rights Reserved.

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