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Tribology International 32 (1999) 469480

www.elsevier.com/locate/triboint

A review of vibration and acoustic measurement methods for thedetection of defects in rolling element bearings

N. Tandon a,*, A. Choudhury b

a ITMME Centre, Indian Institute of Technology, Hauz Khas, New Delhi 110016, Indiab Department of Mechanical Engineering, Regional Engineering College, Silchar 788010, India

Received 5 May 1999; received in revised form 1 September 1999; accepted 12 October 1999

Abstract

A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings is presented inthis paper. Detection of both localized and distributed categories of defect has been considered. An explanation for the vibrationand noise generation in bearings is given. Vibration measurement in both time and frequency domains along with signal processingtechniques such as the high-frequency resonance technique have been covered. Other acoustic measurement techniques such assound pressure, sound intensity and acoustic emission have been reviewed. Recent trends in research on the detection of defectsin bearings, such as the wavelet transform method and automated data processing, have also been included. 2000 Elsevier ScienceLtd. All rights reserved.

Keywords: Rolling element bearings; Bearing defects; Condition monitoring; Vibrations; Acoustics

1. Introduction

Rolling element bearings find widespread domesticand industrial applications. Proper functioning of theseappliances depends, to a great extent, on the smooth andquiet running of the bearings. In industrial applications,these bearings are considered as critical mechanicalcomponents and a defect in such a bearing, unlessdetected in time, causes malfunction and may even leadto catastrophic failure of the machinery. Defects in bear-ings may arise during use or during the manufacturingprocess. Therefore detection of these defects is importantfor condition monitoring as well as quality inspection ofbearings. Different methods are used for detection anddiagnosis of bearing defects; they may be broadly classi-fied as vibration and acoustic measurements, temperaturemeasurements and wear debris analysis. Among these,vibration measurements are the most widely used. Sev-eral techniques have been applied to measure thevibration and acoustic responses from defective bear-ings; i.e., vibration measurements in time and frequency

* Corresponding author. Tel./fax: +91-11-685-7658.

E-mail address: [email protected] (N. Tandon)

0301-679X/99/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved.

PII: S0 3 0 1 - 6 7 9 X ( 9 9 ) 0 0 0 7 7 - 8

domains, the shock pulse method, sound pressure andsound intensity techniques and the acoustic emissionmethod.

A lot of research work has been published, mostly inthe last two decades, on the detection and diagnosis ofbearing defects by vibration and acoustic methods. Someof these works have also been reviewed by researchers.Tandon and Nakra [1] presented a detailed review of thedifferent vibration and acoustic methods, such asvibration measurements in time and frequency domains,sound measurements, the shock pulse method and theacoustic emission technique, for condition monitoring ofrolling bearings. Some of the vibration and wear debrisanalysis techniques such as vibration, shock pulse, spikeenergy, spectrographic oil analysis, ferrography and chipdetection have been reviewed by Kim and Lowe [2] witha particular reference to railway freight cars. A briefreview of vibration monitoring techniques in time andfrequency domains and their results on rolling elementbearings have been presented by Mathew and Alfredson[3]. McFadden and Smith [4] and Kim [5,6] have alsopresented reviews on some specific techniques for con-dition monitoring of rolling bearings. Therefore, theobjective of the present study is to update these reviewsby incorporating recent works and the advanced tech-

niques adopted in bearing defect detection.


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470 N. Tandon, A. Choudhury / Tribology International 32 (1999) 469480

2. Vibration and noise generation in bearings

Several studies [713] have been conducted to explainthe mechanism of vibration and noise generation in bear-ings. Bearings act as a source of vibration and noise due

to either varying compliance or the presence of defectsin them. Radially loaded rolling element bearings gener-ate vibrations even if they are geometrically perfect. Thisis because of the use of a finite number of rollingelements to carry the load. The number of rollingelements and their position in the load zone change withbearing rotation, giving rise to a periodical variation ofthe total stiffness of the bearing assembly. This variationof stiffness generates vibrations commonly known asvarying compliance vibrations [7,8]. When the bearingraces are assumed as continuous systems, the changingdirection of the contact forces applied by the rolling

elements may cause flexural or ring-mode vibration ofthe races even if they are geometrically perfect [9,10].However, the presence of a defect causes a significant

increase in the vibration level. Bearing defects may becategorized as distributed or local. Distributed defectsinclude surface roughness, waviness, misaligned racesand off-size rolling elements [811]. The surface fea-tures are considered in terms of their wavelength com-pared with the Hertzian contact width of the rollingelement raceway contacts. Surface features of wave-length of the order of the contact width or less are termedroughness, whereas longer-wavelength features aretermed waviness [11]. Distributed defects are causedby manufacturing error, improper installation or abrasivewear [12,13]. The variation in contact force betweenrolling elements and raceways due to distributed defectsresults in an increased vibration level. The study ofvibration response due to this category of defect is,therefore, important for quality inspection as well ascondition monitoring.

Localized defects include cracks, pits and spalls onthe rolling surfaces. The dominant mode of failure ofrolling element bearings is spalling of the races or therolling elements, caused when a fatigue crack beginsbelow the surface of the metal and propagates towards

the surface until a piece of metal breaks away to leavea small pit or spall. Fatigue failure may be expeditedby overloading or shock loading of the bearings duringrunning and installation [13]. Electric pitting or cracksdue to excessive shock loading are also among the differ-ent types of bearing damage described in the literature[1317]. Whenever a local defect on an element interactswith its mating element, abrupt changes in the contactstresses at the interface result which generates a pulseof very short duration. This pulse produces vibration and

noise which can be monitored to detect the presence ofa defect in the bearing.

3. Vibration response due to localized defects

Two approaches have been adopted by researchers forcreating localized defects on bearings to study theirvibration response. One is to run the bearing until failureand monitor the changes in their vibration response

[5,1822]. Usually the failure is accelerated by eitheroverloading, overspeeding or starving the bearings oflubricants [5,21,22]. The other approach is to intention-ally introduce defects in the bearings by techniques suchas acid etching, spark erosion, scratching or mechanicalindentation, measure their vibration response and com-pare it with that of good bearings [2334]. In some ofthese studies the size of the simulated defects has beenquantified and varied [26,2933]. The former approachof life tests is quite time-consuming. On the other hand,the testing of bearings with simulated defects is muchquicker but preparation of the defective bearings requires

special techniques.Several techniques have been applied to measure andanalyse the vibration response of bearings with localizeddefects. These techniques are not totally independent;rather, in many cases, they are complementary to oneanother.

3.1. Time-domain approach

The simplest approach in the time domain is tomeasure the overall root-mean-square (RMS) level andcrest factor, i.e., the ratio of peak value to RMS value ofacceleration. This method has been applied with limitedsuccess for the detection of localized defects [30,32].Some statistical parameters such as probability densityand kurtosis have been proposed for bearing defectdetection [35,36]. The probability density of accelerationof a bearing in good condition has a Gaussian distri-bution, whereas a damaged bearing results in non-Gaus-sian distribution with dominant tails because of a relativeincrease in the number of high levels of acceleration[35]. However, Mathew and Alfredson [3] have reportedobtaining a near-Gaussian distribution for some dam-aged bearings also. Instead of studying the probabilitydensity curves, it is often more informative to examine

the statistical moments of the data, defined as

Mx

xnP(x) dx n1, 2, 3, , m, (1)

where P(x) is the probability density function of instan-taneous amplitude x. The first and second moments arewell known, being the mean value and the variance,respectively. The third moment normalized with respectto the cube of standard deviation is known as the coef-ficient of skewness. The fourth moment, normalizedwith respect to the fourth power of standard deviation,


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471N. Tandon, A. Choudhury / Tribology International 32 (1999) 469480

is quite useful. This is called kurtosis and is given bythe expression

kurtosis, b2

(xx)4P(x) dx

s4 , (2)

where x is the mean.Dyer and Stewart [36] first proposed the use of kur-

tosis for bearing defect detection. For an undamagedbearing with Gaussian distribution, the kurtosis value isclose to 3. A value greater than 3 is judged by itself to bean indication of impending failure and no prior history isrequired. However, one disadvantage is that the kurtosisvalue comes down to the level of an undamaged bearing(i.e., 3) when the damage is well advanced. Therefore,it has been suggested to measure kurtosis in selected fre-quency bands [36]. White [37] studied the effectivenessof this method under a simulated condition. Severalother studies [27,28,34,36,38] have also shown the effec-tiveness of kurtosis in bearing defect detection but insome cases [3,5,40] the method could not detect theincipient damage effectively. Kurtosis has not become avery popular method in industry for the condition moni-toring of bearings.

Local defects can also be detected in the time domainby displaying the vibration signal on an oscilloscope orplotting it on a chart recorder and observing the presenceof periodic peaks due to impact of the rolling elementwith the defects [26,29,40,41]. Gustafsson and Tallian

[40] proposed a method of defect detection based on thenumber of peaks crossing a preset voltage level.

Some bandpass filtering techniques have also beenproposed in the time domain. The principle is based onthe fact that structural resonances are excited in the high-frequency zone due to impulsive loading caused, forexample, from spalling of the races or rolling elementsand can be detected by a transducer whose resonant fre-quency is tuned to it. The shock pulse method [42],which works on this principle, uses a piezoelectric trans-ducer having a resonant frequency based at 32 kHz(some instruments based on resonant frequency around100 kHz have also been used). The shock pulses causedby the impacts in the bearings initiate damped oscil-lations in the transducer at its resonant frequency.Measurement of the maximum value of the damped tran-sient gives an indication of the condition of rolling bear-ings. Low-frequency vibrations in the machine, gener-ated by sources other than rolling bearings, areelectronically filtered out. The shock pulse value gener-ated by good bearings due to surface roughness has beenfound empirically to be dependent upon the bearing borediameter and speed. This value, called the initial value,is subtracted from the shock value of the test bearing toobtain a normalized shock pulse value. The maximum

normalized shock value is a measure of the bearing con-dition. Shock pulse meters are simple to use so that semi-skilled personnel can operate them. They give a singlevalue indicating the condition of the bearing straight-away, without the need for elaborate data interpretationas required in some other methods.

The shock pulse method has gained wide industrialacceptance and has been reported to be successful in thedetection of rolling element bearing defects[3,5,34,39,43]. Some investigators [31,44,45] havereported that the method could not effectively detectdefects at low speeds. However, Butler [43] has reportedsuccess of the shock pulse method in the detection ofdefects in low-speed spherical roller bearings in a paperproduction line. An on-line bearing condition monitoringtechnique based on the shock pulse method has beensuggested by Morando [46].

3.2. Frequency-domain approach

Frequency-domain or spectral analysis of the vibrationsignal is perhaps the most widely used approach of bear-ing defect detection. The advent of modern fast Fouriertransform (FFT) analysers has made the job of obtainingnarrowband spectra easier and more efficient. Both low-and high-frequency ranges of the vibration spectrum areof interest in assessing the condition of the bearing.

The interaction of defects in rolling element bearingsproduces pulses of very short duration whenever thedefect strikes or is struck owing to the rotational motionof the system. These pulses excite the natural fre-

quencies of bearing elements and housing structures,resulting in an increase in the vibrational energy at thesehigh frequencies. The resonant frequencies of the indi-vidual bearing elements can be calculated theoretically[1,47,48].

It is difficult to estimate how these resonances areaffected on assembly into a full bearing and mountingin a housing. However, it is indicated [24] that reson-ances are not altered significantly. These natural fre-quencies are usually more than 5 kHz [1]. Therefore,monitoring the increase in the level of vibrations in thehigh-frequency range of the spectrum is an effectivemethod of predicting the condition of rolling elementbearings and has been used successfully by severalinvestigators [3,21,23,24,26]. Catlin [49] has indicatedthat the natural frequency for which the wavelengthclosely matches the pulse length, is most stronglyexcited. Several parameters such as arithmetic mean,geometric mean and correlation have been suggested[50,51] to quantify the differences in spectra for goodbearings and damaged bearings.

Each bearing element has a characteristic rotationalfrequency. With a defect on a particular bearing element,an increase in vibrational energy at this elementsrotational frequency may occur. These characteristic


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defect frequencies can be calculated from kinematic con-siderations; i.e., the geometry of the bearing and itsrotational speed [1,23,26,40,52,53]. For a bearing witha stationary outer race, these frequencies are given bythe following expressions:

cage frequency, wcws2

1dD

cos a , (3)

ball spinning frequency, wbDws

2d1

d2

D2cos2 a , (4)

outer race defect frequency, wodZwc

Zws

2d1

d

Dcos a , (5)

inner race defect frequency, widZ(wswc)

Zws

21

d

Dcos a

(6)

and

rolling element defect frequency, wre2wb

wsD

d1

d2

D2cos a2 , (7)

where ws is the shaft rotation frequency in rad/s, d is thediameter of the rolling element, D is the pitch diameter,Z is the number of rolling elements and a is the con-tact angle.

For normal speeds, these defect frequencies lie in the

low-frequency range and are usually less than 500 Hz.In practice, however, these frequencies may be slightlydifferent from the calculated values as a consequence ofslipping or skidding in the rolling element bearings [53].Several researchers [24,26,27,32,54,55] have reportedsuccess in bearing defect detection by identifying theserotational frequencies. It has also been observed[24,26,27,32] that, in case of a defect on a movingelement such as the inner race or a rolling element, thespectrum has sidebands about the components at charac-teristic defect frequencies. A typical spectrum [32] dueto an inner race defect is shown in Fig. 1. The sidebandshave been attributed to the time-related changes in defectposition relative to the vibration measuring position [26].Tandon and Choudhury [56] have derived expressionsfor frequencies and relative amplitudes of the variousspectral lines based on the flexural vibration of races dueto a localized defect on one of the bearing elements.

In some work [23,57,58], it has been mentioned thatit is difficult to obtain a significant peak at these fre-quencies in the direct spectrum obtained from a defectivebearing. This is due to the fact that noise or vibrationfrom other sources masks the vibration signal from thebearing unless the defect is sufficiently large. Tandonand Nakra [32] have also found that direct spectral

analysis can detect defects of comparatively larger sizesonly. Ray [45] highlighted the condition under whichbearing defect detection becomes difficult.

Osuagwu and Thomas [57] have suggested an expla-nation for the absence of defect frequencies in the spec-trum in terms of the average and shift effect produced

by the variation of the impact period and the intermodu-lation effect. In this study the power cepstrum wasshown to be an effective diagnostic technique. Powercepstrum is defined as the power spectrum of the logar-ithmic power spectrum [35]. Tandon [33] has reportedthat cepstrum can detect outer race defects effectivelybut failed to detect inner race defects.

In order to improve the signal-to-noise ratio and makethe spectral analysis more effective, some signal pro-cessing techniques have been reported. Braun and Datner[59] have suggested signal decomposition into periodiccomponents and a processing method based on averag-ing techniques. The adaptive noise cancelling (ANC)technique has also been proposed to improve the signal-to-noise ratio in bearing fault diagnosis [60]. Envelopedetection or the high-frequency resonance technique(HFRT) is an important signal processing techniquewhich helps in the identification of bearing defects byextracting characteristic defect frequencies (which maynot be present in the direct spectrum) from the vibrationsignal of the defective bearing. A review of the tech-nique has been presented by McFadden and Smith [4].Each time a defect strikes its mating element, a pulse ofshort duration is generated that excites the resonancesperiodically at the characteristic frequency related to the

defect location. The resonances are thus amplitude-modulated at the characteristic defect frequency. Bydemodulating one of these resonances, a signal indica-tive of the bearing condition can be recovered. In prac-tice, the signal is bandpass-filtered around one of theresonant frequencies, thus eliminating most of theunwanted vibration signals from other sources. Thisbandpass-filtered signal is then demodulated by anenvelope detector in which the signal is rectified andsmoothed by low-pass filtering to eliminate the carrieror bandpass-filtered resonant frequency. The spectrum ofthe envelope signal in the low-frequency range is thenobtained to get the characteristic defect frequency of thebearing. The process of extraction of demodulated spec-tra by the high-frequency resonance technique is shownin Fig. 2.

This technique has been used extensively and its suc-cess has been demonstrated by several investigators[23,25,3032,6180]. A single-mode vibration modelhas been developed by McFadden and Smith [6264] toexplain the appearance of various spectral lines owingto different defect locations in the demodulated spec-trum. They have suggested that the sidebands around thedefect frequency are a result of the modulation of carrierfrequency by loading and transmission path. This model


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Fig. 1. A typical spectrum obtained from a rolling element bearing with an inner race defect.

Fig. 2. The process of extraction of demodulated spectra by HFRT.


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has been extended by Su and Lin [79] to characterizethe vibrations of bearings subjected to various loadings.Martin and Thorpe [80] have suggested normalizationof the envelope-detected frequency spectra of the faultybearing with respect to the healthy bearing to givegreater sensitivity to the detection of defect frequencies.

The limitation of this technique is that, with advanceddamage, the defect frequencies may become submergedin the rising background level of the spectrum [4,74].This may happen due to the reduced severity of impactswhich are generated so frequently that the leading edgeof the impact is buried in the decay of the previousimpact [4]. However, Burgess [74] argues that this isbecause of the reduced difference in amplitudes of therandom noise floor and the defect peak heights, whichalso become random as the defect progresses.

In recent years, the wavelet transform method hasbeen suggested by some researchers [8183] to extractvery weak signals for which FFT becomes ineffective.Wavelet transform provides a variable-resolution timefrequency distribution from which periodic structuralringing due to repetitive force impulses, generated uponthe passing of each rolling element over the defect, aredetected. The adaptive timefrequency resolution makesit superior vis-a-vis FFT. Mori et al. [83] proposed adiscrete wavelet transform theory which can detect adefect even at pre-spalling stage.

3.3. Proximity transducer technique

The literature discussed so far has mostly considered

casing-mounted transducers such as accelerometers andvelocity pick-up for the measurement of vibration. Someresearchers have also used non-contact type displace-ment or proximity transducers for condition monitoringof rolling element bearings. In these studies, the trans-ducer senses the displacement of the outer race directlyas the rolling elements pass under it. Thus the extraneousvibrations of the housing structure are reduced or elimin-ated and the signal-to-noise ratio is improved. However,the installation of these probes is difficult as it not onlyinvolves drilling and tapping of the bearing housing butalso fine adjustment of the gap between the probe andthe outer race, which can change due to such conditionsas vibration, dirt and thermal expansion. This monitoringtechnique is popularly known as REBAM (RollingElement Bearing Activity Monitor).

Philips and associates [8486] first reported the useof this technique. They used a fibre optic sensor tomeasure the elastic deformation of the outer ring. Philips[86] has also introduced the concept of ball speed ratio(BSR), which has been found to be a good parameterfor bearing performance monitoring. Harker and Hansen[87] used a high-gain eddy current transducer andreported case histories for rotating machinery healthmonitoring using this technique. REBAM has been

found to be effective in bearing defect detection in someother studies as well [34,88]. Kim [6,8991] has appliedthis technique to a radially loaded deep groove ball bear-ing. He has reported an effective diagnostic method forouter and inner race damage to predict the angularlocation of the damaged spots. He also suggested that

this technique, coupled with HFRT, will be very effec-tive for fault detection in rolling element bearings [91].

3.4. Automated diagnostic system

The techniques discussed above need a humaninterpreter to analyse the results. However, the amountof data collected from modern instruments may be sovast that an automated system providing concise andreliable assessment of the machinery condition becomesnecessary. This is equally true for rolling element bear-ings.

In 1989, Li and Wu [92] proposed a monitoringscheme based on pattern recognition. The techniqueemploys short-time-signal processing techniques toextract useful features from bearing vibration to be usedby a pattern classifier to detect and diagnose the defect.To overcome the shortcomings of this method, bispectralanalysis of vibration has been applied to detect the pres-ence of the characteristic defect frequency and its har-monics [93]. The pattern recognition technique is thenapplied to classify the condition of a bearing based onits bicoherence. An automatic fault diagnosis system forball bearings, based on processing of time-domain signa-

tures and a pattern recognition technique, has also beenreported [94].

In recent times, artificial neural networks haveemerged as a popular tool for signal processing and pat-tern classification tasks, and are suitable for conditionmonitoring programs. An artificial neural network canbe defined as a mathematical model of the human brainand has the ability to learn to solve a problem, ratherthan having to be preprogrammed with a precise algor-ithm. Baillie and Mathew [95] proposed a model basedon a neural network for fault detection in rolling elementbearings. The system consists of a collection of para-metric time-series models, one for each class of bearingfault to be identified, based on a back-propagation neuralnetwork. This time-domain-based model has the advan-tage that the diagnosis can be performed using very shortdata lengths and is suitable for application in slow- andvariable-speed machinery. However, the main drawbackis that the model cannot handle very large data withoutmisclassifying the fault. To overcome this shortcoming,they have proposed to present the vibration data to theneural network in the frequency domain [96] and havealso proposed that characteristic defect frequencies canbe used for classification of faults. In separate research[97], a neural bearing analyser model has been


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developed taking only certain areas of the vibration spec-trum and using a back-propagation network.

4. Vibration response due to distributed defects

As discussed earlier, distributed defects include sur-face irregularities like roughness, waviness or off-sizerolling elements. The vibration response for these defectshas been studied mostly in the frequency domain.Vibration produced by surface roughness or shorter-wavelength features has been studied by Sayles andPoon [98] and is found to be significant only when theasperities break through the lubricant film and contactthe opposing surface. However, longer-wavelength fea-tures (waviness) or varying rolling element diameterhave a more dominant effect on the vibration level. Thefrequency limit in which waviness produces a significantvibration level has been assessed [11,99] and is foundto be below 60 times the rotational speed.

Systematic studies of vibration produced by wavinesswere first made by Tallian and Gustafsson [8]. The five-degree-of-freedom motion of the outer race con-sidered as a body which is rigid except for the contactdeformations was analysed with a linearized dynamicmodel. The waviness orders of major importance havebeen assessed and are found to have a simple numericalrelationship with the number of rolling elements. Meyeret al. [9] proposed an analytical model for flexuralvibration of the stationary race under axial load due towaviness on the moving race or unequal ball diameter.

The amplitudes of the important frequency componentshave also been predicted in the model. The model wasextended later by Choudhury and Tandon [10] for radi-ally loaded rolling bearings. Vibration forces producedby axially loaded angular contact ball bearings under theinfluence of waviness on various bearing elements havebeen studied by Wardle [100]. He considered anadditional mass (housing) attached to the stationary outerring. Analytical results for radial and axial vibrationshave been verified experimentally [101]. A linear modelhas been proposed by Yhland [102] for vibration of theshaft bearing system due to form errors. Waviness ordersof major importance for the vibration response of atapered roller bearing have been investigated experimen-tally by Ohta and Sugimoto [103]. Sunnersjo [12] hasstudied the vibration of a radially loaded bearing due toinner race waviness or varying roller diameter, andfound the significant peaks to occur at harmonics of shaftand cage speed, respectively, with a sideband spaced atroller passage frequency in the case of inner race wav-iness. Similar cage harmonics have been observed byHine [104] for circumferential ball diameter variation inthe bearing of a turbopump in the main engine of a spaceshuttle. The effects of surface irregularities have alsobeen investigated [105,106] to explain the spectral lines

observed in the demodulated spectra of a normal taperedroller bearing.

Since many of the frequencies resulting from distrib-uted defects coincide with those due to localized defects,it becomes difficult to identify from frequency infor-mation alone whether a peak at a particular frequency is

due to a localized or a distributed defect. Therefore, ithas been suggested [107,108] that, in addition to fre-quency information, the amplitudes of the spectralcomponents should also be studied for defective bear-ings.

Some studies [109111] have also been carried out toestimate the roughness or waviness of the races or theballs in bearings by vibration analysis. Laser Dopplervibrometry has been proposed for surface profilemeasurements in rolling bearings [112].

5. Acoustic emission response from defective

bearings

Acoustic emission (AE) is the phenomenon of transi-ent elastic wave generation due to a rapid release ofstrain energy caused by a structural alteration in a solidmaterial under mechanical or thermal stresses. Gener-ation and propagation of cracks, growth of twins, etc.associated with plastic deformation are among the pri-mary sources of AE. Hence it is an important tool forcondition monitoring through non-destructive testing.AE instrumentation consists of a transducer, mostly ofthe piezoelectric type, a preamplifier and a signal-pro-

cessing unit. The transducers, which have very highnatural frequency, have a resonant-type response. Thebandwidth of the AE signal can also be controlled byusing a suitable filter in the preamplifier. The most com-monly measured AE parameters are ringdown counts,events and peak amplitude of the signal. These are dem-onstrated on a typical AE signal shown in Fig. 3. Ring-down counts involve counting the number of times theamplitude exceeds a preset voltage level (thresholdlevel) in a given time and gives a simple number charac-teristic of the signal. An event consists of a group ofringdown counts and signifies a transient wave. Theadvantage of acoustic emission monitoring overvibration monitoring is that the former can detect thegrowth of subsurface cracks, whereas the latter candetect defects only when they appear on the surface. Itis also important to note that the energy released byneighbouring components in the vibrational frequencyrange (up to 50 kHz), which often masks the vibrationalenergy released from a defective rolling element bearing,do not affect the AE signal released in the very highfrequency range.

Several studies have been conducted to investigate theAE response of defective bearings. In the case of AEmonitoring of local defects also, two approaches of life


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Fig. 3. A typical acoustic emission burst signal.

tests [113117] and simulated defects [118120] havebeen adopted by researchers. In 1979, Rogers [121] sug-gested the application of acoustic emission as a measureof the condition of slow-speed anti-friction bearings ofslewing cranes in offshore gas production platforms.

Yoshioka and Fujiwara [113,114] have shown that AEparameters can detect defects before they appear in thevibration acceleration range and can also detect the poss-ible sources of AE generation during a fatigue life test ofthrust ball bearings. They also measured the propagationinitiation time of cracks and the propagation time untilflaking occurs by a combination of AE parameters andvibration acceleration [115]. The source locator systemwas also improved later by introducing two AE sensorsin the system and measuring the difference of arrivaltimes of acoustic emission signals at the sensors[116,117]. Acoustic emission signals have been shownto detect defects in the form of a fine scratch on theinner race of axially loaded angular contact ball bearingsbut at low speeds only [44,118]. Tandon and Nakra [119]have demonstrated the usefulness of some acoustic emis-sion parameters, such as peak amplitude and count, forthe detection of defects in radially loaded ball bearingsat low and normal speeds. The distribution of events bycounts and peak amplitude has also been used for qualityinspection of bearings to judge whether the bearing is anew or regenerated one [122]. Tan [123] has suggestedthat the measurement of area under the amplitudetimecurve is a preferred method for defect detection in rollingelement bearings. He also applied the adaptive noise

cancellation technique to filter out background AE noiseemitted by other machine components [124]. The useful-ness of demodulated AE signals in detecting defects inrolling element bearings has been demonstrated by someresearchers [120,125127].

6. Acoustic noise response

Measurement of acoustic noise can also be used forthe detection of defects in rolling element bearings.These measurements are normally carried out in twomodes: sound pressure and sound intensity. Sound press-ure generated by good bearings has been studied by sev-eral researchers [128131], but very little literature isavailable on sound measurements as a defect detectiontechnique. Igarashi and Yabe [132] have shown the use-fulness of sound pressure measurement for the detectionof defects in axially loaded ball bearings. The role ofsurface irregularities in the production of noise in rollingcontact has been studied with the help of sound pressuremeasurement [133].

Sound intensity measurement, a comparatively recenttechnique, has also been tried successfully for the detec-tion of defects in rolling element bearings [5]. Soundintensity is defined as the time-averaged rate of flow ofsound energy through unit area. Unlike sound pressure,it is a vector quantity and the two-microphone intensityprobe has directional characteristics. Sound intensity inthe frequency domain can be obtained from the imagin-


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ary part of the cross-spectrum between the signals oftwo closely spaced microphones [134]. A special two-microphone probe is used for intensity measurements.The imaginary part of the cross-spectrum can beobtained directly using a dual-channel FFT analyser.Tandon and Nakra [135] have shown the usefulness of

sound intensity measurement as a bearing diagnostictechnique and have concluded that, for this purpose, itis more effective than sound pressure measurement.Spectral analysis of demodulated signals received by asound meter has been suggested as a monitoring tool forwayside detection of railroad roller bearing defects[136].

The detectability of defects by sound measurementmay be affected by sources other than bearing noiseunless adequate precautions are taken to isolate the lat-ter. A partial or full acoustic enclosure lined with sound-absorbing material is usually constructed around the testbearing for this purpose. Design of such anechoicchambers has also been discussed in the literature[133,137].

7. Conclusions

From a review of studies on vibration and acousticmeasurement techniques for the detection of defects inrolling element bearings, it is seen that the emphasis ison vibration measurement methods. Although literatureis available for the detection of both localized and dis-tributed defects, the former has drawn the attention of a

greater number of researchers. Vibration in the timedomain can be measured through parameters such asoverall RMS level, crest factor, probability density andkurtosis. Among these, kurtosis is the most effective.The shock pulse method has also gained wide indus-trial acceptance.

Vibration measurement in the frequency domain hasthe advantage that it can detect the location of the defect.However, the direct vibration spectrum from a defectivebearing may not indicate the defect at the initial stage.Some signal processing techniques are, therefore, used.The high-frequency resonance technique is the mostpopular of these and has been successfully applied byseveral researchers. For this technique, the procedure ofsignal processing is well established and a good expla-nation of the resultant demodulated spectrum is alsoavailable. The method has a disadvantage that advanceddamage is difficult to detect by this method. In recentyears, the wavelet transform method has been suggestedto extract very weak signals for which Fourier transformbecomes ineffective.

Very few studies have been carried out on acousticnoise measurements for the detection of bearing defects.Measurements of both sound pressure and sound inten-sity have been used for this purpose. The sound intensity

technique seems to be better than sound pressuremeasurements for bearing diagnostics.

Acoustic emission measurements have also been usedsuccessfully for detecting defects in rolling elementbearings. Some studies indicate that these measurementsare better than vibration measurements and can detect a

defect even before it appears on the surface. Demodu-lation of AE signals for bearing defect detection has alsobeen suggested.

In recent years, attention has also been focused on theautomated interpretation of data for bearing diagnostics.The pattern recognition technique and neural networkshave been applied to data obtained from vibrationmeasurements in both time and frequency domains forthe detection of defects in rolling element bearings.

References

[1] Tandon N, Nakra BC. Vibration and acoustic monitoring tech-niques for the detection of defects in rolling element bearings

a review. Shock Vibr Digest 1992;24(3):311.

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