A method for extreme data reduction of Acoustic Emission (AE) data with application in machine failure diagnosis

Transcription

1 A method for extreme data reduction of Acoutic Emiion (AE) data with application in machine failure diagnoi Critián Molina icuña and Chritoph Höweler 2 Laboratorio de ibracione Mecánica, Univeridad de Concepción Edmundo Larena 27 int., Concepción, Chile 2 IAMIS - Indutrial Service & Reearch; Condition Baed Maintenance Gaborn 33, 5262 Aachen, Germany {crimolin@udec.cl, chritoph.hoeweler@viami.de} Abtract The ue of AE in machine failure diagnoi ha increaed over the lat year. Mot AE-baed failure diagnoi trategie ue digital ignal proceing and thu require the ampling of AE ignal. High ampling rate are required for thi purpoe (e.g. 2 MHz), which lead to tream of large amount of data. Thi ituation i aggravated if fine reolution and/or multiple enor are required. Thee fact combine to produce bulky amount of data typically in the range of GByte, for which ufficient torage pace and efficient ignal proceing algorithm are required. Thi ituation probably explain why, in practice, AE-baed method conit motly in the calculation of calar quantitie uch a RMS, Kurtoi, etc., and the analyi of their evolution in time. While the calar-baed approach offer the advantage of maximum data reduction; it ha the diadvantage that mot part of the information contained in the raw AE ignal i lot unrecoverably. Thi work preent a method offering extreme data reduction, while keeping the mot important information conveyed by the raw AE ignal, which i ueful for failure diagnoi purpoe. The propoed method i very imple: it conit on the contruction of a ynthetic, unevenly ampled ignal which envelope the AE burt preent on the raw AE ignal in a triangular hape. The contructed ignal which we call TriSignal alo permit the etimation of mot calar quantitie typically ued. But more importantly, it contain the information of the time of occurrence of the burt, which i key for failure diagnoi. Furthemore, we how how the TriSignal can be ued to compute the TriSpectrum, an etimator of the frequency content of the AE envelope. Introduction During the lat decade we have experienced an increae in the ue of AE for condition monitoring of machine []. The mot important advance have been made in the problem of bearing failure detection. In thi cae, the detection i baed on the major difference between the AE generated by healthy and faulty bearing. In the firt cae, the AE ignal ha a noie-like hape; wherea when a fault develop, the reulting AE contain a number of burt. Although thi i the particular cae for bearing, there are other element in which a failure doe alo reult in AE burt; thee include gear [2][3][4][5] and crack [6]. Thi ituation jutifie why the interet ha been focued on the detection of the preence of the burt in the AE ignal. In cae where the fault i the only ource of burt-type AE, the calculation and trending of calar value (e.g. Kurtoi, RMS) i commonly ufficient for thi tak. Thi i the cae of bearing, and probably explain why the majority of reearch in thi ubject ha relied on thi calar-baed approach [7][8][9][]. Some invetigator have reported intereting advance including the correlation of the calar quantitie with the failure ize [][2]. Mot of thee reearche ue tet benche compriing only bearing (one of them being the tet object). Thu, the only ource of burt-type AE i indeed the baring fault. Thi i illutrated in Fig., where the AE meaured on the bearing cae of a bearing tet bench uch a the decribed are preented for the nonfaulty and faulty cae. The AE reulting from the bearing in good condition i a random ignal preenting no recognizable burt. Differently, a ucceion of burt are evidenced when the faulty bearing (in thi cae outer race defect) i intalled. The burt are generated by the interaction of the rotating element with the faulty

2 Non Faulty. p =.26, pp =.3977, RMS =.2775, CF =.854, K = Faulty. p =.75632, pp =.4659, RMS =.289, CF = 26.67, K = Figure : AE meaured on the bearing cae of a bearing tet bench. (a) Non-faulty bearing. (b) Faulty bearing (outer race fault). Nomenclature of calar value: p : peak; pp : peak-to-peak; RMS: Root Mean Square; CF: cret factor; K: Kurtoi. zone. The plot in Fig. alo include the magnitude of calar value typically ued. Note how all thee value increae, thu evidencing the preence of the fault. A expected in a cae like thi, the Kurtoi i the mot enitive parameter. The advantage of the calar baed approach i that the calar quantitie can be calculated by hardware and, thu, can be recorded uing low-cot data acquiition ytem. Baed on the previou example one would not need a different method, ince the calar-baed approach i ufficient for the detection of the fault. However, real machine do alo include other mechanical component, being gear tranmiion probably the mot often found. Even though everal quetion regarding the behaviour of AE in gear till are ubject of reearch, there i enough evidence of the burt-type AE generated a the (nonfaulty) gear teeth meh [5]. In a ituation like thi, the calar quantitie calculated from the AE meaured on, for example, a gear tranmiion, would be largely influenced by the burt coming from the gear tooth interaction. Hence, under the preence of a bearing fault, the calar-baed approach would only be uceful if the burt originated by the fault are large enough to produce recognizable change in the calar value. From our experience, thi i rarely the cae. Conider the AE ignal hown in Fig.2a, which wa meaured on the cae of a planetary gearbox without failure. The figure alo preent the value of the calar quantitie typically ued. Figure 2b how the ituation for the AE meaured on the ame gearbox, but with a faulty planet gear (localized eeded defect). The only calar quantitie incrementing their value with repect to the non-faulty ituation were the RMS (3.7%) and the Kurtoi (.6%), but i arguable if thi increae i ufficient to be indicative of the preence of the fault, ince AE from gear are alo enitive to change in load and peed [5]. Keep in mind, however, that the information related to the failure i preent in the raw AE; it i only that the calar value are not much enitive to it. The problem i that thi information can not be recovered from the calar quantitie; that i, the evidence of the preence of the failure i lot unrecoverably. The previou example illutrate the need for a different approach, capable of overcoming thi ituation. Recognizing that the ucceion of burt preent on the AE ignal make it eentially a cyclotationary ignal, a traightforward olution would be to crutinize the frequeny tructure of the cyclic amplitude change. Probably the bet alternative to do thi i by uing the Spectral Correlation Denity (SCD) or the Spectral Coherence (SCoh) [3], ince both preent the content of the amplitude change in the frequency and cyclic frequency domain imultaneouly. However, the computing cot of doing thi (in term of time conumption and proceing requirement) might be too high for ome application. The envelope pectrum i another cyclotationary tool which demand le computing cot, but i not a powerful a the SCD and SCoh [4]. In mot cae, the envelope pectrum would probably be the bet olution. However, the envelope pectrum it i till a vector containing a number of element that can be large depending 2

3 Nonfaulty.p=.8879,pp=3.736,RMS=.49238,CF=38.347,K= Faulty. p =.877, pp = , RMS =.55974, CF = , K = (a) (b) Figure 2: Acoutic emiion meaured on a planetary gearbox (a) non-faulty. (b) Localized planet defect. Nomenclature of calar value: p : peak; pp : peak-to-peak; RMS: Root Mean Square; CF: cret factor; K: Kurtoi. on the reolution and maximum frequency. In ome application, the envelope might till be expenive in term of torage requirement. Thi work preent an alternative method offering extreme data-reduction poibilitie, while keeping the mot important information conveyed by the raw AE-ignal, which i ueful for machine failure diagnoi. Another advantage of the method i it implicity. We preent the method in the following ection. 2 The TriSignal method One of the mot important application of AE in condition monitoring i in low-peed machinery. Thi i becaue at low peed, the energy releaed due to the interaction of the element under the preence of a fault i ometime not ufficient to produce actual movement, but ufficient to produce acoutic wave. Thu, meaurement from vibration enor uualy do not contain information related with the failure; wherea data from AE enor have more chance of carrying thi valuable information. When dealing with low-peed machine, ignal containing everal haft revolution are normally needed to obtain the deired frequency reolution. For example, a reolution of. Order would be a typical choice for the analyi. If the haft rotate at Hz, then a long ignal i mut be recorded. If the ampling rate i 2 MHz and the double-preciion format i ued, then 6 MByte of data are generated. The data amount grow when multiple channel and repetitive meaurement are needed, a would be the cae for typical continuo monitoring ytem. Large torage capabilitie would then be required to tore the meaurement. Moreover, the proceing of uch amount of data demand high computing requirement and efficient ignal proceing algorithm. In practice, all thi reult in expenive ytem and i probably the reaon why the calar-baed approach ha prevailed, at the expene of great information lo. The propoed method wa developed with the objective of keeping the mot poible information relevant for failure diagnoi, which i contained in the raw AE ignal, while offering large data reduction, thu providing a olution to the afore mentioned ituation. The method conit in the contruction of a ynthetic, unevenly ampled ignal, which envelope the AE burt preent in the raw AE ignal in a triangular hape, o that each burt in the ignal i enveloped by a triangle, a hown in Fig.3a. Note from thi figure that three coordinate pair are needed for thi purpoe; however it i only required to obtain four calar value from the AE ignal. Three calar value contain the time information decriptive of the burt: t i i the tart time of the burt; t i m i the time of maximum amplitude; and t i e i the time of burt end. The fourth calar value (y i n) contain the maximum amplitude of the burt (i =,2,... i the burt index). The amplitude of the firt and third coordinate pair are only required to cloe the triangle, hence they arbitrarily take the value of zero (i.e. y i = y i e = ). The TriSignal i then formed by the ucceion of all triangle enveloping the burt in the AE ignal, a preented in Fig.3b. From thi definition, the TriSignal can be interpreted a an unevenly, underampled envelope of the raw AE ignal. Although the idea of the TriSignal might appear implitic, we how in the next ection that it can be ued to contruct powerful tool for failure diagnotic. 3

4 olt (a) olt (b) Figure 3: Contruction of the TriSignal. (a) Enveloping a ingle burt in triangular hape. (b) The TriSignal (red) i formed by the ucceion of triangle enveloping the burt in the raw AE ignal. 2. Data reduction feature Let u conider we would like to determine the condition of a bearing rotating at Hz. In order to do thi a frequency reolution of. Hz ha been choen, for which an AE ignal of length ha been meaured. The ampling rate i MHz and the data i tored in double-preciion format. Let u alo upoe the bearing ha an outer race defect and the BPFO i approximately 7 Hz. Table reume the torage requirement of the raw AE and the reult of different proceing technique. In the cae of the envelope, a downampling factor of 2 ha been conidered. Item Data amount Data reduction Raw AE ( ) 8 MByte AE-Envelope ( ).4 MByte 99.5% w.r.t. raw AE TriSignal ( ) 2.24 kbyte 99.44% w.r.t. AE-Envelope % w.r.t. raw AE Scalar value (5 value) 4 Byte 98.24% w.r.t. TriSignal 99.99% w.r.t. AE-Envelope % w.r.t. raw AE Table : Storage requirement and data reduction. Note in thi cae the AE-envelope perform greatly in term of data reduction with repect to the raw AE (reduction of 99.5%). The TriSignal perform even better giving a further reduction of 99.44% (with repect to the AE-Envelope). A expected, the maximum reduction i obtained with the calar-baed approach. Table 2 how the amount of data generated over a period of month and year, conidering the cae in which an AE-baed continuou monitoring ytem i ued. The ytem perform 2 meaurement each day, each of, and ha 4 channel. The large amount of accumulated data could alo add difficultie in the data handling and backup. Item After month After year Raw AE ( ) 9.6 GByte 6.8 GByte AE-Envelope ( ) 48 MByte 584 MByte TriSignal ( ) kbyte 3.3 MByte Scalar value (5 value) 4.8 kbyte 58.4 kbyte Table 2: Accumulated data after a period of time. 4

5 olt Figure 4: Contruction of the TriSignal from the AE envelope. Color legend: raw AE (blue); AE envelope (green); TriSignal (olid red); tatic threhold (dahed red). 2.2 TriSignal in the time domain The mot challenging part in building the TriSignal i determining when each AE Burt tart and when it end. Thi i alo a crucial point within the method, becaue the reult that can be obtained from the TriSignal are highly dependent on the correct decription of the burt. The eaiet way to perform thi i by defining a tatic threhold and an hyterei parameter to avoid the diviion of a ingle burt into everal burt of horter duration. With thi method, the beginning and end of each burt are determined by the time of threhold croing meeting the hyterei criteria. Thi method can be applied to the raw AE, a it wa preented in Fig.3 or to a filtered verion of the raw AE ignal, where the filter i ued to enhance the burt with repect to the background noie and improve the burt detection proce. Spectral Kurtoi [5][6] or Cyclotationaritybaed quantitie (e.g. SCD, SCoh) can be computed from a hort portion of the AE-ignal containing ome burt for identification of the optimum filtering range. Depending on the characteritic of the background noie, a moving threhold could be a better alternative. Thi can be a moving RMS or a imilar quantity. Alternatively, the method can be applied to the envelope of the AE, a preented in Fig.4. The envelope being a meaure of the energy flow in time, it uually reult more practical to detect the burt on the envelope of the AE. In thi cae, it mut be conidered that the value obtained for each triangle (i.e. t i, t i m and y i m) correpond to value of the energy flow rather than value of the actual AE ignal. The choice of the mot appropriate method for burt detection, a well a the election of the hyterei parameter will depend on the individual characteritic of the AE ignal being meaured; more preciely, on the pecific feature of the burt with repect to the continuou part of the ignal. We have found the detection baed on the croing of the AE envelope acro a fixed threhold to have acceptable reult in the experiment we have conducted till date, which include AE from faulty bearing, from the mehing of pur gear and from hydraulic motor. The benefit of the TriSignal are not only the data reduction, but the quantitie that can be obtained from it. For example, it i poible to calculate etimator of time-domain calar value typically determined from the raw AE, uch a the peak value, rie time (equal to t i m t i ), fall time (equal to t i e t i m), event duration (equal to t i e t i m), number of burt, event denity, etc. Other calar value uch a the RMS and Kurtoi cannot be etimated from the TriSignal becaue the information of the background noie i not contained. If thee (and/or other) value are required, it would be neceary to calculate them from the raw AE an tore them together with the TriSignal. Note the information of each of thee value i available for each burt, o it i even poible to crutinize their tatitic through, for example, their probability denity function. A a completely ynthetic ignal, it i poible to ue the reult from the calar value of each burt for filtering or ource eparation purpoe. Conider, for example, an AE ignal containing two familie of repetitive burt from two different ource. Both ource generate burt having the ame frequency range, but one of them produce burt with larger duration. In a cae like thi, the event duration calar value for each triangle can be ued to dicriminate between a burt coming from one or the other ource. Thi proce lead to the contruction of two TriSignal, one for each ource, which can be analyzed eparately. Figure 5 how a imilar 5

6 Figure 5: TriSignal ued for eparation of AE burt from different ource (TriSignal determined from the AE envelope). example in which the peak value wa ued to dicriminate between two ource; the TriSignal hown contain only the triangle with peak value higher than TriSignal in the frequency domain Once the TriSignal ha been contructed, a natural tep forward conit in the inpection of it periodicitie. In the cae of the traditional envelope, thi i uually done by crutinizing the envelope magnitude pectrum, which i obtained by calculation of the Fourier tranform of the envelope ignal. Conidering it characteritic, it i logical to proceed imilarly with the TriSignal. However, thi i not a traightforward, ince from it definition, the TriSignal i an unevenly ampled ignal. A uch, it doe not meet the requirement for the calculation of the magnitude pectrum by direct computing it Fourier tranform. Thi iue can be olved by uing ome method. The mot imple would be interpolating the TriSignal o a to have an evenly-ampled verion of it, and then calculate it Fourier tranform. However, a reported by [7], thi method perform poorly epecially if the ignal preent long pacing between ucceive point, a i the cae for the TriSignal. The Lomb-Scargle normalized periodiogram (Lomb pectrum in what follow) i another poible method which i not ubjected to thi problem. It i baed on the evaluation of ine and coine of different frequencie together with the unevenly ampled ignal only at the time correponding to the point of the latter [7]. That i, in the cae of the TriSignal, at the time t i, t i m and t i e for all hit of the ignal portion under analyi. We call the pectrum obtained from the calculation of the Lomb Spectrum of the TriSignal, the TriSpectrum, which reult in an etimator of the hape and therefore the frequency ditribution of the AE envelope. After calculation of the mean µ and variance σ 2 of all data point of the TriSignal, the TriSpectrum G yy ( f ) i calculated by G yy ( f ) = 2σ 2 + 2σ 2 ) )] 2 [ i j (t i j µ co2π f (t i j τ ) i j co 2 2π f (t i j τ ) )] 2 [ i j (t i j µ in2π f (t i j τ ) i j in 2 2π f (t i j τ () where i i the burt index, and j =, m, ande. The frequency-dependent time contant τ i defined by where f i the frequency in Hz. tan4π f τ = i j in2π ft i j i j co4π ft i j (2) 6

7 2 TriSignal.5.5 olt Figure 6: Portion of TriSignal, non-faulty planetary gearbox. Color legend: raw AE (blue); AE envelope (green); TriSignal (red). The Lomb pectrum i then obtained with a low-frequency limit of f low = /T, where T i defined { a } the total time-pan between all data point of the TriSignal conidered for the calculation (i.e. T = max t i j min { t i j } ). Since it i highly probable that ome data point of the TriSignal will be paced much cloer than the average pacing conidering all data point, it i even poible to obtain tatitically ignificant reult in the Lomb pectrum for frequencie above the Nyquit frequency that would be obtained hould all data point be equally-paced in the conidered time-pan T. The Lomb pectrum algorithm preented i a low algorithm; however, an approximation of it can be calculated much fater by making ue of the FFT algorithm [7]. Following, we preent an example of the ue of the TriSpectrum calculated from the AE ignal meaured on a ingle-tage pur-gear planetary gearbox. The gearbox ha three equally paced planet gear, each with 26 teeth. The un gear ha 8 teeth and the annulu gear ha 72 teeth. The gearbox i ued in reduction mode with a rotational peed of the un gear of f S = 3 Hz and a contant load of 65 Nm i applied to the output of the tranmiion. Thi gearbox pertain to group A according to the claification propoed in [4]: Planetary gearboxe with equally-paced planet gear and in-phae gear mehing procee, o that the mehing of all wheel inide the gearbox produce the burt part of the AE activity at the ame time. For f S = 3 Hz, the gear meh frequency i 432 Hz and the rotational peed of the carrier plate i 6 Hz. Acoutic emiion were meaured with a enor intalled on the outer part of the annulu gear. The TriSignal wa contructed baed on the envelope of the meaured AE, a hown in Fig.6. The TriSpectrum wa calculated from the TriSignal uing the previouly defined methodology; the portion around the firt gear meh harmonic i preented in Fig.7a. The modulation oberved a ideband around the gear meh frequency (marked with ) are due to the varying tranfer path between the ource poition of the AE (which continuouly change during operation of the gearbox) and the (fixed) poition of the enor. The component recognized in the TriSpectrum are thu normal for a gearbox of thee characteritic and, therefore, the diagnoi i good condition of the unit. For comparion purpoe the envelope pectrum of the AE i hown in Fig.7b. Note the imilarity with the hape of the TriSpectrum. The numeric curor in figure 7a and 7b are referenced in the next paragraph. A localized defect wa eeded in one tooth of one planet gear. The defect wa accomplihed uing an electrical engraving machine, Fig.8, and i conidered a urface defect over the complete flank of one tooth. The meaurement wa taken in the ame manner decribed previouly and the ame procedure wa followed to obtain the TriSignal and the TriSpectrum. Thee are hown in Fig.9 and Fig.a, repectively. The TriSpectrum now reveal component not preent before, marked with the numeric curor. The pace between each of thee component and the gear meh frequency equal the relative rotational frequency of the planet gear with repect to the carrier plate, which in thi cae i f P C = 6.6 Hz. Their preence i indicative of the defect (note they were not preent in the non-faulty ituation, ee curor in Fig.7a and Fig.7b). The envelope pectrum i hown in Fig.b, from which again the ame information offered by the TriSpectrum i obtained. In both faulty and non-faulty cae, a ignal length of 8 econd wa ued. The raw AE wa ampled at a rate of 2.5 MHz. In a double-preciion format each of thee ignal ue 36 MByte. The envelope ha 7

8 Envelopetmagnitudetpectrum TriSpectrum Power X:745.7 Y: X:t448.9 Y:t.4886 X:t45.7 Y:t X: Y:72.256e α [Hz] α [Hz] (a) (b) Figure 7: Portion around the firt gear meh harmonic of the (a) TriSpectrum and (b) envelope pectrum. Nonfaulty planetary gearbox. Figure 8: Localized defect in one planet gear wheel. TriSignal 2.5 olt Figure 9: Portion of TriSignal, faulty planetary gearbox. Color legend: raw AE (blue); AE envelope (green); TriSignal (red). been downampled to khz, for which a torage pace of.44 MByte i needed. In the non-faulty cae, the TriSignal ha 262 triangle occupying a torage pace of.36 MByte. In the faulty cae, the TriSignal ha 59 triangle and ue.34 MByte. Thi repreent an average data reduction of 99.9% with repect to the raw AE, and of 75.7% with repect to the envelope. 8

9 .5 TriSpectrum. Envelopetmagnitudetpectrum Power.5.5 X:645.7 Y: X: Y: X:t45.7 Y:t.2926 X:t448.9 Y:t α [Hz] (a) α [Hz] (b) Figure : Portion around the firt gear meh harmonic of the (a) TriSpectrum and (b) envelope pectrum. Faulty planetary gearbox. 3 Some final remark The TriSpectrum appear a an intereting alternative to the envelope pectrum. However, the latter i of coure preferred; if there are enough reource to bae the monitoring in the envelope, then thi hould be preferred. The only idea behind the TriSignal and the TriSpectrum i to provide an alternative to the envelope when i deired to keep the minimum tored data. We finally emphaize again the critical importance of detecting the burt correctly on the contruction of the TriSignal. Conidering that different mechanical ytem have pecific feature, a previou analyi of the AE of each particular cae will be, in general, neceary. In cae where the burt are clearly obervable in the ignal, thi repreent no problem. In other cae, it might be needed to perform certain analyi to define a filter which better iolate the burt from the ret of the ignal. If the mechanical ytem produce burt in the non-faulty condition (e.g. in gear), then a filter defined to iolate the normal burt would be a good choice. The hypothei here i that the burt generated by a fault-related ource will probably hare the ame frequency range or at leat part of it, which i often the cae. On the other hand, if the ytem doe not produce burt in the non-faulty condition (e.g. in bearing), a reonant zone of the enor can be choen. Alternatively, meaurement can be done while generating artificial AE ource on the cae of the machine and then electing a filter by analyzing it frequency content. The artificial AE ource can be generated in practice by impacting the cae with a crewdriver or by breaking a graphite pencil lead on the cae of the machine. Alternative to elect the mot appropriate filter include Spectral Kurtoi and cyclotationary-baed tool, altough le ofiticated technique can be ued in ome cae. For example, the db pectrum difference can be ued uing two portion of the ame ignal, one including one or more burt and the other including only the continuou part. Fig. illutrate thi method applied on the AE ignal meaured on the hydraulic motor. Note in thi cae it i actually not neceary to ue any method, becaue the burt are clearly viible in the raw AE ignal. Still, the method reult in a ignal where the burt are even more evident. Note alo how the amplitude modulation preent in the portion between the burt ha alo been removed. The amplitude modulation i due to electronic interference and, therefore, i not real AE. Since the interference i preent in both ignal portion, the db pectrum difference i maximum in the frequency region where the AE burt predominate. 4 Concluion A method for data reduction of acoutic emiion ha been preented, which additionaly retain the key information for machine failure diagnoi. The method conit in the contruction of the TriSignal, a ynthetic ignal enveloping the characteritic burt preent in the raw AE. In ome of the example hown, the reduction wa of % with repect to the raw AE ignal, and of 99.44% with repect to the envelope of the AE. Further proceing of the TriSignal uing the Lomb-Scargle periodogram reult in the TriSpectrum, which i an etimator of the frequency tructure of the envelope and, therefore, a valuable tool for failure diagnoi. The mot important part of the method relie in the correct identification of the burt. Previou to the application of the method on a particular cae, an tudy hould be preformed to analyze the characteritic of the AE and 9

10 db db 5 Individual pectra db difference f, [khz] (a) Figure : db pectrum difference method ued to improve ignal-to-noie ratio of the AE ignal. (a) Top: individual pectra of portion including burt (red) and portion not including burt (blue); bottom: db pectrum difference. (b) Raw AE ignal (blue) and filtered AE ignal (red). (b) find the filter which maximize the preence of the burt with repect to the ret of the ignal. Acknowledgement The firt author thank FONDECYT for the upport of the Project 7. Reference [] D. Mba and Raj B.K.N. Rao, Development of acoutic emiion technology for condition monitoring and diagnoi of rotating machine; bearing, pump, gearboxe, engine and rotating tructure, The Shock and ibration Diget, 38(), 26. [2] C. Tan and D. Mba, Correlation between acoutic emiion activity and aperity contact during mehing of pur gear under partial elatohydrodynamic lubrication, Tribology Letter, 2() pp.63-67, September 25. [3] R.I. Raja Hamzah and D. Mba, The influence of operating condition on acoutic emiion (ae) generation during mehing of helical and pur gear, Tribology International, 42() pp.3-4, 29. [4] icuña CM, Effect of operating condition on the Acoutic Emiion (AE) from planetary gearboxe, Appl Acout (23), [5] Álvaro Barrueto Novoa, Critián Molina icuña, New apect concerning the generation of Acoutic Emiion (AE) in pur gear and the influence of operating condition, Surveillance 7, Chartre, France, 23 October. [6] D.G. Aggeli, E.Z. Kordato, T.E. Matika, Acoutic emiion for fatigue damage characterization in metal plate, Mechanic Reearch Communication 38 (2) pp.6-. [7] N. Tandon, G.S. Yadava, K.M. Ramakrihna, A comparion of ome condition monitoring technique for the detection of defect in induction motor ball bearing, Mechanical Sytem and Signal Proceing 2 (27) pp [8] A. Choudhury, N. Tandon, Application of acoutic emiion technique for the detection of defect in rolling element bearing, Tribology International 33 (2) pp [9] A Morhain and D Mba, Bearing defect diagnoi and acoutic emiion, Proc. Intn Mech. Engr ol. 27 Part J: J. Engineering Tribology, pp ,23.

11 [] N. Tandon and B. C. Nakra, Defect Detection in Rolling Element Bearing by Acoutic Emiion Method, Journal of Acoutic Emiion, olume 9, Number, pp.25-28, 99. [] Saad Al-Doary, R.I. Raja Hamzah, D. Mba, Obervation of change in acoutic emiion waveform for varying eeded defect ize in a rolling element bearing, Applied Acoutic 7 (29) pp [2] Abdullah M. Al-Ghamd, David Mba, A comparative experimental tudy on the ue of acoutic emiion and vibration analyi for bearing defect identification and etimation of defect ize, Mechanical Sytem and Signal Proceing 2 (26) pp [3] Jérôme Antoni, Cyclic pectral analyi in practice, Mechanical Sytem and Signal Proceing 2 (27) pp [4] R.B. Randall, J. Antoni, S. Chobaard, The relationhip between pectral correlation and envelope analyi in the diagnotic of bearing fault and other cyclotationary machine ignal, Mechanical Sytem and Signal Proceing (2) 5(5) pp [5] Dwyer, R., Detection of non-gauian ignal by frequency domain Kurtoi etimation, Acoutic, Speech, and Signal Proceing, IEEE International Conference on ICASSP 83. (olume:8), pp.67-6, 983. [6] Jérôme Antoni, The pectral kurtoi: a ueful tool for characteriing non-tationary ignal, Mechanical Sytem and Signal Proceing 2 (26) pp [7] William H. Pre, Saul A. Teukolky, William T. etterling, Brian P. Flannery, Numerical Recipe: The Art of Scientific Computing, Cambridge Univerity Pre; 3rd edition, ISBN: , 27.