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ISSN 2454-535XVol. 1, No. 1, August 2015

International Journal of Mechanical Engineering Research and Technology

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Int. J. Mech. Eng. Res. & Tech 2015 Harsh Patidar and R K Mandloi, 2015

STUDY OF DETECTION OF DEFECTS IN ROLLINGELEMENT BEARINGS USING VIBRATION AND

ACOUSTIC MEASUREMENT METHODS—A REVIEW

Harsh Patidar1* and R K Mandloi1

*Corresponding Author: Harsh Patidar,[email protected]

In this paper a review of defect detection in rolling element bearings using vibration and acousticmeasurement methods is considered. Localized and distributed categories of defect detectionhave been reviewed. Vibration measurement in both time and frequency domains along withsig-nal processing techniques such as the high-frequency resonance technique have beenconsidered. As it is important to monitor the condition of the bearings and to know the severity ofthe defects before they cause serious damages. Therefore, the study of vibrations generated bythese defects plays an important role in quality inspection as well as for condition monitoring ofthe rolling element bearings. Acoustic emission measurements for the detection of defects inroller bearings have been investigated in this paper. Other techniques like sound pressure andacoustic emission of acoustic measurement have been covered.

Keywords: Vibration, Acoustic, Rolling element bearings, Defect detection, Conditionmonitoring, Bearing defects, Kurtosis

INTRODUCTIONRolling element bearings have wide use indomestic and industrial applications.Functioning of these appliances depends, toa great extent, on the smooth and quiet runningof the bearings. Bearings, in industrialapplications are considered as criticalmechanical components and a defect in sucha bearing, if not detected in time causesmalfunction and may even causes catastrophic

ISSN 2454 – 535X www.ijmert.comVol. 1, No. 1, August 2015

© 2015 IJMERT. All Rights Reserved

Int. J. Mech. Eng. Res. & Tech 2015

1 Department of Mechanical Engineering, M.A.N.I.T, Bhopal 462003, India.

failure of the machinery. Defects in bearingsmay arise during its working or during themanufacturing process. Therefore detection ofthese defects is very important from point ofcondition monitoring as well as qualityinspection of bearings (Tandon andChoudhury, 1999). Rolling element bearingsare manufactured by assembling differentcomponents: The rolling elements, the outerring and the inner ring, which are in contact

Review Article

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under heavy dynamic loads and relatively highspeeds (Zeki Kiral and Hira Karagu¨lle, 2003).Different methods are used for detection anddiagnosis of bearing defects; they may bebroadly classified as vibration and acousticmeasurements, temperature measurementsand wear debris analysis. Among these,vibration measurements are the most widelyused. Several techniques have been appliedto measure the vibration and acousticresponses from defective bearings, i.e.,vibration measurements in time and frequencydomains, the shock pulse method, soundpressure and sound intensity techniques andthe acoustic emission method.

A lot of research work has been published,mostly in the last two decades, on the detectionand diagnosis of bearing defects by vibrationand acoustic methods. Some of these workshave also been reviewed by researchers.Tandon and Nakra (1992) presented a detailedreview of the different vibration and acousticmethods, such as vibration measurements intime and frequency domains, soundmeasurements, the shock pulse method andthe acoustic emission technique, for conditionmonitoring of rolling bearings. McFadden andSmith (1984) and Kim (1984) have alsopresented reviews on some specif ictechniques for condition monitoring of rollingbearings.

White (1984) describes a method forsimulating the machinery fault signals whichare impulsive in nature and analyzed them. Thebasic understandings of the rolling elementbearing vibrations for a defected case and awell-established model that considers the loaddistribution around the circumference of therolling element bearing and the impulse

response of the bearing structure are proposedin McFadden and Smith (1984). The modesummation method is employed to find thevibratory response of the bearing subjectedto radial or axial load for the cases of differentdefect locations. The bearing vibration signalsare modeled as a combination of differentsources such as fault, modulation due to non-uniform loading, flexural bearing modes,machinery induced vibrations and noise inWang and Kootsookos (1998).

BEARING DEFECTSBearings act as a source of vibration andnoise due to either varying compliance or thepresence of defects in them. Radially loadedrolling element bearings generate vibrationseven if they are geometrically perfect. This isbecause of the use of a finite number of rollingelements to carry the load. The number ofrolling elements and their position in the loadzone change with bearing rotation, giving riseto a periodical variation of the total stiffnessof the bearing assembly. This variation ofstiffness generates vibrations commonlyknown as varying compliance vibrations(Tallian and Gustafsson, 1965; and Sunnersjo,1978). However, the presence of a defectcauses a significant increase in the vibrationlevel. The defects in the rolling elementbearings may arise mainly due to followingreasons such as; improper design of thebearing or improper manufacturing ormounting, misalignment of bearing races,unequal diameter of rolling elements,improper lubrication, overloading, fatigue,uneven wear etc. The rolling element bearingdefects/faults may be categorized asdistributed defects and localized defects.

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Distributed DefectsDistributed defects include surface roughness,waviness, misaligned races and off-size rollingelements (Tallian and Gustafsson, 1965; Meyeret al., 1980; and Choudhury and Tandon,1998). Distributed defects are caused bymanufacturing error, improper installation orabrasive wear (Sunnersjo, 1985; and Washo,1996). The variation in contact force betweenrolling elements and raceways due todistributed defects results in an increasedvibration level. The study of vibration responsedue to this category of defect is, therefore,important for quality inspection as well ascondition monitoring.

Localized DefectsLocalized defects include cracks, pits andspalls on the rolling surfaces. The dominantmode of failure of rolling element bearings isspalling of the races or the rolling elements,caused when a fatigue crack begins below thesurface of the metal and propagates towardsthe surface until a piece of metal breaks awayto leave a small pit or spall. Fatigue failure maybe expedited by overloading or shock loadingof the bearings during running and installation(Washo, 1996).

Two approaches have been adopted byresearchers for creating localized defects onbearings to study their vibration response. Oneis to run the bearing until failure and monitorthe changes in their vibration response (Nishioet al., 1979; Igarashi et al., 1980; and Kim,1984). The other approach is to intentionallyintroduce defects in the bearings bytechniques such as acid etching, sparkerosion, scratching or mechanical indentation,measure their vibration response andcompare it with that of good bearings (Dyer,

1973; and Tandon and Nakra, 1992 and1993). The former approach of life tests is quitetime-consuming. On the other hand, the testingof bearings with simulated defects is muchquicker but preparation of the defectivebearings requires special techniques.

As discussed earlier, distributed defectsinclude surface irregularities like roughness,waviness or off-size rolling elements. Thevibration response for these defects has beenstudied mostly in the frequency domain. Sincemany of the frequencies resulting fromdistributed defects coincide with those due tolocalized defects, it becomes difficult to identifyfrom frequency information alone whether apeak at a particular frequency is due to alocalized or a distributed defect.

BEARING CHARACTERISTICFREQUENCIESA machine with a rolling element bearing isrunning at certain speed; now a defect beginsto develop, the vibration spectrum changesproduced in bearing. The occurrencefrequencies of the shocks resulted from thedefects in the bearings are called bearingdefect frequencies or bearing characteristicsfrequencies. Each bearing element has abearing characteristic frequency. The peakswill occur in the spectrum at these frequenciesdue to increase in vibrational energy. Initiationand progression of defects or faults on rollingelement bearing generate specific andpredictable characteristic of vibration. A modelpresented by Tandon and Choudhury (Tandonand Choudhury, 1997; and Choudhury andTandon, 1998) predicted frequency spectrumhaving peaks at these frequencies. Defects incomponents of rolling element bearing such

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as inner race, outer race, rolling elements andcage generate a specific defect frequenciescalculated theoretically from the belowequations (Shyam Patidar and PradeepKumar Soni, 2013):

• FTF - Fundamental Train Frequency(frequency of the defected cage):

f (Hz) = S[ ] ...(1)

• BPFI - Ball Pass Frequency of the Inner race(frequency produce when the rollingelements roll across the defect of innerrace):

f (Hz) = S[1+ ] ...(2)

• BPFO - Ball Pass Frequency of Outer race(frequency produce when the rollingelements roll across the defect of outerrace):

f(Hz) = S[1- ] ...(3)

• BSF - Ball Spin Frequency (circularfrequency of each rolling element as itspins):

f(Hz) = S[1- ] ...(4)

• Rolling Element Defect Frequency or

2 x BSF

f(Hz) = S[1- ] ...(5)

DEFECT DETECTIONTECHNIQUESVibration Measurement MethodsThe vibration measurement methods can beclassified as in time and in frequency domains.A brief review on the monitoring techniques in

time and in frequency domain can be found in(Mathew and Alfredson, 1984). Honarvar andMartin (1997) and Martin and Honarvar (1995)use the third and fourth moment of the vibrationsignals known as skewness and kurtosis,respectively, for bearing failure detection. Louet al. (2000) propose a method based onextracting the dynamic model of the bearingsystem from the experimental vibration signalsto design a proper fault detection filter.Recently time-frequency domain analysis hasbecome popular. The wavelet method is usedby the researchers (Tse et al., 2001; Nikolaouand Antoniadis, 2002; and Rajesh Kumar andManpreet Singh, 2013) in condition monitoringof rolling element bearings due to its superiorityin time and frequency resolution whileprocessing the vibration signals.

Time Domain AnalysisThe simplest approach in the time domain isto measure the overall Root-Mean-Square(RMS) level and Crest Factor (Cf), i.e., the ratioof peak value to RMS value of acceleration.

RMS(x) = …(6)

Cf= ...(7)

where, N is the number of discrete points andrepresents the signal from each sampledpoint.This method has been applied withlimited success for the detection of localizeddefects (Tandon and Nakra, 1993). Theresultant RMS values are compared withrecommended values to determine thecondition of a bearing (Tandon, 1994)however, this method is not sensitive to detectsmall or early-stage defects (Downham, 1980).The crest factor is the ratio of peak

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acceleration over RMS. Ingarashi et al. (1980)has reported that the value of the crest factorfor good bearing is approximately five. Atadvance stages of material wear, bearingdamage propagates, RMS increases, andcrest factor decreases. Kurtosis is anotherimportant parameter to indentify the health ofbearing. The equation to calculate the value ofkurtosis is given by:

k = ...(8)

where, x(n) is the time series, is the meanvalue of the data, is the variance of the dataand N is the total number of data points. A goodsurface finish bearing has a theoreticalkurtosis of 3, and when the surface finishdeteriorates value of kurtosis increases, butkurtosis is insensitive to loads and speeds(Kurfess et al., 2006).

Dyer and Stewart (1978) first proposed theuse of kurtosis for bearing defect detection. Avalue greater than 3 is judged by itself to bean indication of impending failure and no priorhistory is required. However, onedisadvantage is that the kurtosis value comesdown to the level of an undamaged bearing(i.e., 3) when the damage is well advanced.Therefore, it has been suggested to measurekurtosis in selected frequency bands. White(White, 1984) studied the effectiveness of thismethod under a simulated condition. Severalother studies (Rogers, 1979; and Tan, 1991)have also shown the effectiveness of kurtosisin bearing defect detection but in some cases(Dyer and Stewart, 1978; and Kim, 1984) themethod could not detect the incipient damageeffectively. Kurtosis has not become a verypopular method in industry for the condition

monitoring of bearings (Tandon andChoudhury, 1999).

Kurtosis value, Crest factor, Impulse factorand Clearance factor are non-dimensionalstatistical parameters. Impulse and Clearancefactors have similar effects like Crest factor andKurtosis value (Alguindigue et al., 1993). Li andPickering (1992) proved that the Impulse factor,Kurtosis value, Crest factor and Clearancefactor are all sensitive to incipient fatiguespalling. Tuncay Karacay and Nizami Akturk(2009) investigated that the peak-to-peak value,RMS, Crest factor and kurtosis are only showsthe damage at the ball bearing but do not giveinformation about the location of defect, e.g.,inner race, outer race, cage or the roller.

Frequency-Domain AnalysisFrequency-domain or spectral analysis of thevibration signal is perhaps the most widelyused approach of bearing defect detection.The advent of modern Fast Fourier Transform(FFT) analysers has made the job of obtainingnarrowband spectra easier and more efficient.Both low and high-frequency ranges of thevibration spectrum are of interest in assessingthe condition of the bearing.

The interaction of defects in rolling elementbearings produces pulses of very shortduration whenever the defect strikes or isstruck owing to the rotational motion of thesystem. These pulses excite the naturalfrequencies of bearing elements and housingstructures, resulting in an increase in thevibrational energy at these high frequencies.The resonant frequencies of the individualbearing elements can be calculatedtheoretically (Smith, 1982; Tandon and Nakra,1992; and Tandon and Choudhury, 1999).

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It is difficult to estimate how theseresonances are affected on assembly into afull bearing and mounting in housing.Therefore, monitoring the increase in the levelof vibrations in the high-frequency range of thespectrum is an effective method of predictingthe condition of rolling element bearings andhas been used successfully by Dyer andStewart (1978).

The frequency domain analysis is moreuseful as it identifies the exact nature of defectin the bearings. These frequencies of the ballbearing depend on the bearing characteristicsand are calculated from the relations shownbelow in section 3 (Love, 1944; and ShyamPatidar, Pradeep Kumar Soni, 2013). Fornormal speeds, these defect frequencies liein the low-frequency range and are usually lessthan 500 Hz.

In practice, however, these frequencies maybe slightly different from the calculated valuesas a consequence of slipping or skidding inthe rolling element bearings (Attel Manjunathand Girish, 2013). Several researchers (Dyerand Stewart, 1978; and Tandon and Nakra,1993) have reported success in bearing defectdetection by identifying these rotationalfrequencies. It has also been observed that, incase of a defect on a moving element such asthe inner race or a rolling element, thespectrum has sidebands about thecomponents at characteristic defectfrequencies. Tandon and Nakra (1993) havefound that direct spectral analysis can detectdefects of comparatively larger sizes only. Inthis study the power cepstrum was shown tobe an effective diagnostic technique. Powercepstrum is defined as the power spectrum ofthe logarithmic power spectrum (Prasad,

1987). Tandon (1994) has reported thatcepstrum can detect outer race defectseffectively but failed to detect inner racedefects.

A review of the technique has beenpresented by McFadden and Smith (1984).Each time a defect strikes its matingelement, a pulse of short duration isgenerated that excites the resonancesperiodically at the characteristic frequencyrelated to the defect location. Theresonances are thus amplitude modulatedat the characteristic defect frequency. Bydemodulating one of these resonances, asignal indicative of the bearing condition canbe recovered. In practice, the signal is bandpass-filtered around one of the resonantfrequencies, thus eliminating most of theunwanted vibration signals from othersources. This band pass-filtered signal isthen demodulated by an envelope detectorin which the signal is rectified and smoothedby low-pass filtering to eliminate the carrieror band pass-filtered resonant frequency.The spectrum of the envelope signal in thelow-frequency range is then obtained to getthe characteristic defect frequency of thebearing.

This technique has been used extensivelyand its success has been demonstrated byseveral investigators (Tandon and Nakra, 1992and 1993). This may happen due to thereduced severity of impacts which aregenerated so frequently that the leading edgeof the impact is buried in the decay of theprevious impact (McFadden and Smith, 1984).

In condition monitoring of the bearings,frequency analysis of the time domain signalplays an important role.

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The rate of the repetition of impulsesgenerated due to the interaction of the defectsand rolling elements is observed in thefrequency spectra of the time domain signal.For small defect size these impulses mergewith other vibrations and it becomes difficultto identify them in time domain. In such casetheir repetition rate can easily be observedfrom frequency spectra.

Envelope analysis or amplitudedemodulation is another popular techniqueused to detecting incipient failure of rollingelement bearing. This method is also knownby different names such as high frequencyresonance technique [McFadden and Smith,1984; Prashad et al., 1985; Lai and Reif,1989; and Su and Lin, 1992), amplitudedemodulation (White, 1991), demodulatedresonance analysis and narrow band envelopeanalysis (McMahon, 1991; and Azovtsev et al.,1994). Envelope analysis or amplitudedemodulation is the technique that can extractthe periodic impacts from the modulatedrandom noise produced within a deterioratingrolling element bearing. This process evenpossible when the signal from the rollingelement bearing is relatively low in energy and‘buried’ within the other vibrations producedfrom the machine.

Time-Frequency Domain AnalysisTime-frequency domain analysis has capabilityto handle both, stationary and non-stationaryvibration signals. This is the main advantageover frequency domain analysis.

Time-frequency analysis can show thesignal frequency components, reveals theirtime variant features. A number of time-frequency analysis methods, such as the Short-

Time Fourier Transform (STFT), Wigner-VilleDistribution (WVD), and Wavelet Transform(WT), have been introduced. STFT method isused to diagnosis of rolling element bearingfaults (Kaewkongka et al., 2003). The basicidea of the STFT is to divide the initial signalinto segments with short-time window and thenapply the Fourier transform to each timesegment to ascertain the frequencies thatexisted in that segment. The advantage ofWavelet Transform (WT) over the STFT is thatit can achieve high frequency resolutions withsharper time resolutions. An enhancedKurtogram method used to diagnosis of rollingelement bearing faults by Dong Wang et al.(2013). This Kurtogram method is based onkurtosis of temporal signals that are filteredby the STFT. The Wigner-Ville distribution isalso a popular time frequency analysis method.The Wigner-Ville distribution not use anywindow function therefore it is free from theinterference between time localization andfrequency resolution. The Wavelet Transform(WT) is the most popular method to diagnosisbearing faults (Rubini and Meneghetti, 2001;and Luo et al., 2003). The average of thewavelet amplitude frequencies on a selectedband that is not affected by resonance ofsystem was used by Rubini and Meneghetti(2001). Dalpiaz and Rivola (1997) comparedthe effectiveness and reliability of wavelettransform method to other vibration analysistechniques. Mori et al. (1996) have predictedthe spalling on the ball bearing by applyingdiscrete wavelet transform to vibration signals.Prabhakar et al. (2002) have proved that thediscrete wavelet transform can be used as aneffective tool for detecting single and multiplefaults in the ball bearings. Wang and Gao(2003) proposed wavelet transform

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conjunction with Fourier transform to enhancefeature extraction. Sun and Tang (2002) haveapplied a singularity analysis on the continuouswavelet transform to diagnosis of bearingfaults. This method spots the lines in thewavelet map that converge to singular pointsat fine scales. Jing and Qu (2000) havedeveloped a de-noising method based onMorlet wavelets for feature extraction that issuccessfully applied it to detect inner race rollerbearing fault. Some researchers (Yen and Lin,2000; Nikolaou and Antoniadis, 2002; andToliyat et al., 2003) studied an advancedtransform known as wavelet packet transformfor vibration monitoring. Yunlong and Zhenxiang(2010) presented a method of combination ofkurtosis and wavelet analysis to diagnosis ofrolling bearing fault. This method can detectquick and effectively bearing faults observedthrough experiment.

Acoustic EmissionAcoustic Emission (AE) is the phenomenonof transient elastic wave generation inmaterials under stress. When the material issubjected to stress at a certain level, a rapidrelease of strain energy takes place in the formof elastic waves which can be detected bytransducers placed on it. Plastic deformationand growth of cracks are among the mainsources of acoustic emission in metals. AEtechnique is, therefore, widely used in non-destructive testing for the detection of crackpropagation and failure detection in rotatingmachinery. The signal is generated andmeasured in the frequency range which isgreater than 100 kHz. AE monitoring has anadded advantage that it can even detect thegrowth of subsurface cracks whereas vibrationmonitoring can normally detect a defect when

it appears on the surface (Tandon andChoudhury, 2000).

The most commonly measured AEparameters are ringdown counts, events andpeak amplitude of the signal. Ringdown countsinvolve counting the number of times theamplitude exceeds a preset voltage (thresholdlevel) in a given time and gives a simplenumber characteristic of the signal. An eventconsists of a group of ringdown counts andsignifies a transient wave (Tandon andChoudhury, 2000).

Several studies have been conducted toinvestigate the AE response of defectivebearings. In the case of AE monitoring of localdefects also, two approaches of life tests(Collacott, 1977; and Yoshioka and Fujiwara,1984) and simulated defects (Tandon andNakra, 1993; and Yoshioka and Takeda, 1994)have been adopted by researchers. Rogers(1979) suggested the application of acousticemission as a measure of the condition of slow-speed anti-friction bearings of slewing cranesin offshore gas production platforms. Yoshiokaand Fujiwara (1984) have shown that AEparameters can detect defects before theyappear in the vibration acceleration range andcan also detect the possible sources of AEgeneration during a fatigue life test of thrustball bearings. They also measured thepropagation initiation time of cracks and thepropagation time until flaking occurs by acombination of AE parameters and vibrationacceleration (Yoshioka and Fujiwara, 1982).The source locator system was also improvedlater by introducing two AE sensors in thesystem and measuring the difference of arrivaltimes of acoustic emission signals at thesensors (Yoshioka and Fujiwara, 1988; and

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extract very weak signals for which Fouriertransform becomes ineffective.

Very few studies have been carried out onacoustic noise measurements for the detectionof bearing defects. Measurements of bothsound pressure and sound intensity have beenused for this purpose. The sound intensitytechnique seems to be better than soundpressure measurements for bearingdiagnostics.

Acoustic emission measurements havealso been used successfully for detectingdefects in rolling element bearings. Somestudies indicate that these measurements arebetter than vibration measurements and candetect a defect even before it appears on thesurface. Demodulation of AE signals forbearing defect detection has also beensuggested.

From this study it is seen that all researchersare considering local defects on bearing racesand concluded results. But if vibrationsgenerated in bearings due to the presence ofmultiple defects on their races have beenstudied in time and frequency domains [48],time delay between two successive impulsesdue to multiple defects and the informationrelated to defect angle and number of defectsis clearly observed in time domain analysis.However, when the defect angle is the sameas the angle between two successive balls,the vibration response of bearing having twodefects is the same as the vibration responseof bearing having single defect. It is essentialto mention here that the information related tothe number of defects and the time delaybetween successive impulses could not beseen in the frequency domain analysis. The

Yoshioka, 1992). Acoustic emission signalshave been shown to detect defects in the formof a fine scratch on the inner race of axiallyloaded angular contact ball bearings but at lowspeeds only (Yoshioka and Takeda, 1994; andPrabhu, 1996). Tandon and Nakra (1993) havedemonstrated the usefulness of some acousticemission parameters, such as peak amplitudeand count, for the detection of defects in radiallyloaded ball bearings at low and normalspeeds.

CONCLUSIONFrom a review of studies on vibration andacoustic measurement techniques for thedetection of defects in rolling elementbearings, it is seen that the emphasis is onvibration measurement methods. Vibration inthe time domain can be measured throughparameters such as overall RMS level, crestfactor, probability density and kurtosis. Amongthese, kurtosis is the most effective.

Vibration measurement in the frequencydomain has the advantage that it can detectthe location of the defect. However, the directvibration spectrum from a defective bearingmay not indicate the defect at the initial stage.Some signal processing techniques are,therefore, used. The high-frequencyresonance technique is the most popular ofthese and has been successfully applied byseveral researchers. For this technique, theprocedure of signal processing is wellestablished and a good explanation of theresultant demodulated spectrum is alsoavailable. The method has a disadvantagethat advanced damage is difficult to detectby this method. In recent years, the wavelettransform method has been suggested to

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frequency spectra of single defect and twodefects are found to be identical, i.e., noadditional frequencies are noticed due toimpulses generated with time delay betweentwo defects. However, the magnitudes offrequencies are varying based on the anglesbetween two defects due to change in phaseangles. If multiple defects on bearing races areconsidered then it provides betterunderstanding of vibration generated by usingthe frequency and time domain analysis andin this kurtosis results improved.

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