外文文獻翻譯--使用聲發(fā)射傳感器的低速軸承故障診斷【中文9600字】 【PDF+中文WORD】,中文9600字,PDF+中文WORD,外文文獻翻譯,使用聲發(fā)射傳感器的低速軸承故障診斷【中文9600字】,【PDF+中文WORD】,外文,文獻,翻譯,使用,聲發(fā),傳感器,低速,軸承,故障診斷,中文,9600 Low speed bearing fault diagnosis using acoustic emission sensorsBrandon Van Hecke,Jae Yoon,David HeDepartment of Mechanical and Industrial Engineering,The University of Illinois at Chicago,Chicago,IL 60607,United Statesa r t i c l ei n f oArticle history:Received 3 December 2014Received in revised form 28 October 2015Accepted 29 October 2015Keywords:Bearing faultDiagnosisAcoustic emission sensorLow speeda b s t r a c tIn this paper,a new methodology for low speed bearing fault diagnosis is presented.This acoustic emis-sion(AE)based technique starts with a heterodyne frequency reduction approach that samples AE signalsat a rate comparable to vibration centered methodologies.Then,the sampled AE signal is time syn-chronously resampled to account for possible fluctuations in shaft speed and bearing slippage.Theresampling approach is able to segment the AE signal according to shaft crossing times such that an evennumber of data points are available to compute a single spectral average which is used to extract featuresand evaluate numerous condition indicators(CIs)for bearing fault diagnosis.Unlike existing averagingbased noise reduction approaches that require the computation of multiple averages for each bearingfault type,the presented approach computes only one average for all bearing fault types.The presentedtechnique is validated using the AE signals of seeded fault steel bearings on a bearing test rig.The resultsin this paper have shown that the low sampled AE signals in combination with the presented approachcan be utilized to effectively extract condition indicators to diagnose all four bearing fault types at mul-tiple low shaft speeds below 10 Hz.?2015 Published by Elsevier Ltd.1.IntroductionMost heavy equipment and mechanical devices contain at leastsome type of bearing.Additionally,because it is typical for bear-ings to be utilized in less than ideal working conditions,problemsmay arise and result in premature failures.When a bearing fails,the malfunction can lead to significant downtime,elevated mainte-nance costs,and the potential for a decrease in productivity.Onerecent study presented a rolling bearing fault diagnosis approachusing neural networks and a time/frequency-domain vibrationapproach 1.This study showed that neural networks can aid inthe diagnosis of various motor bearing faults using vibration datagenerated from a bearing test rig.Neural networks have also beenshown to be effective for condition monitoring when usingstatistical-time features 2.Another study focused on rotatingmachinery in general and used a model-based approach for thedetection and diagnosis of mechanical faults 3.This researchdeveloped a nonlinear filtering technique to address complex non-linear vibration responses due to factors such as unbalance,changes in stiffness,and damping of the rotor bearing system.Validation of the aforementioned technique was accomplishedvia the use low signal-to-noise environment simulations.Ref.4presented a fault detection approach based on stator currentmonitoring for in situ bearing faults.In this paper,bearing fault fea-tures were extracted using a combination of noise cancellation andstatistical process control techniques to detect out of control sam-ples due to the degradation of lubrication starved bearings.Oneresearch has presented a two-step data mining approach to classifydefects in plastic bearings 5.This study effectively used empiricalmode decomposition(EMD)to extract time domain condition indi-cators(CIs)which were used as inputs to a supervised learningalgorithm to classify bearing defects.Other studies have presentedeffective bearing defect techniques through the use of local andnonlocal preserving projection 6,trace ratio linear discriminantanalysis 7,or the power spectral density(PSD)analysis of ampli-tude and frequency current-demodulated bearing signals fordirect-drive wind turbines 8.In many industries,the use of low speed rotating machines is astaple for successful operation.Such machines can be found insteel and paper mills,biological applications,and wind turbines.Thus,the monitoring of bearings,shafts and gears in such applica-tions is critical for the proper maintenance of low speed equip-ment.In the low speed bearing fault diagnosis literature,lowspeed has been referred to as in the range from 0.33 Hz to 10 Hz9,whereas significantly lower speed thresholds have been con-sidered as a separate classification range.For example,speedsbelow 0.5 Hz have been viewed as ultra low”10 and if below0.83 Hz considered extremely low”11.In this paper,the bearinghttp:/dx.doi.org/10.1016/j.apacoust.2015.10.0280003-682X/?2015 Published by Elsevier Ltd.Corresponding author.E-mail address:davidheuic.edu(D.He).Applied Acoustics 105(2016)3544Contents lists available at ScienceDirectApplied Acousticsjournal homepage: diagnostic problem with a shaft speed falling within the lowspeed range described in 9 will be addressed.To date,the condition monitoring of rolling element bearingsand other rotating equipment using vibratory analysis is an estab-lished technique and the industry standard.One study investigatedthe use of parametric models of amplitude demodulated vibrationsignals,and the resulting frequency spectra,for bearing faultdetection and diagnosis of roller bearings with defects at a shaftspeed of 1 Hz 12.In that paper,a signal processing techniquewas first used to detect bearing defects and the frequency spectrawere then used to classify the defects.Although effective,thismethodology required visual inspection of the frequency spectrato achieve fault diagnosis.Later,another study developed a lowspeed technology system to measure vibrations resulting fromlow speed rotating machinery 9.This system was centered onseparating the high frequency noise of the machine from the lowfrequency signatures of interest,and validation was presentedusing results from a low speed rotor and gearbox.More recently,for low speed applications,a general model of faulty bearing vibra-tion signals has been established and it was shown that envelope-autocorrelation can be observed in a faulty bearing,but not in thehealthy bearing case 13.Others have sought to develop newaccelerometers and have shown that with the aid of the resonancedemodulation technique,low speed rolling bearing faults can bedetected 14.However,the use of vibration is limited at low speedapplications because the change in energy generated from faults atsuch speeds may not be detectable using traditional accelerometerbased monitoring systems.Thus,researchers have investigated theuse of acoustic emission(AE)sensors and strain gauges for compo-nent monitoring at low speed conditions.AE based studies have shown promising results for incipientfault detection,and recent studies have explored their use forlow speed bearing monitoring.One early investigation looked atthe monitoring of bearings at low speeds 15.In this study,resultscomparing acceleration,shock pulse transducer,acoustic emission,and jerk measurements were presented.Among other conclusionsdrawn,it was mentioned that AE resulted in clear detection of anouter track bearing defect at speeds as low as 0.17 Hz.However,it was also mentioned that the observed AE response could notexplained and that averaging methodologies could not be utilizedbecause the signals did not repeat exactly on a once per revolutionbasis.Another study reported the use of AE for monitoring rollingelement bearings at extremely low shaft speeds from 0.0083 Hz to0.083 Hz 11.In this study it was found that AE measurement isquite sensitive for detecting bearing faults when the bearing isrotating at an extremely low speed,whereas the accelerationenvelope was limited to detecting faults at the lowest shaft speedof 10 Hz.This study utilized an AE pulse count and noted that usingsuch an approach allowed the data to be manageable.Ref.16explored the application of AE for the low speed monitoring ofbearings.This study used the K-means clustering approach to clas-sify line defects on a spherical roller bearing.Although promisingresults were achieved,the study focused on the frequency rangeof 100 kHz to 1 MHz and also relied on a data mining techniquewhich can be time consuming due to the large data requirementfor training and testing of the models.Ref.17 also relied on a datamining approach,presenting a different classification techniquebased on relevance vector machines and support vector machinesto diagnose bearing faults at shaft speeds ranging from 0.33 Hzto 1.33 Hz.In another study,an AE sensor was used to investigatethe incipient fault detection of low speed rolling element bearingsin the frequency range up to 100 kHz 18.This study evaluated anumber of time domain condition indicators using different filterbands to determine the best parameters that can distinguishbetween healthy and faulty inner race bearing signals.However,the goal of this study was to determine effective filter band rangesand time domain condition indicators that can be used to evaluatethe acquired AE signals.Moreover,only an inner race fault wasobserved and the diagnosis of all four bearing fault types couldnot be confirmed using the respective filter band and CIs presentedin the paper.Later,Ref.10 proposed a diagnostic method usingthe AE envelope waveform and obtained results at speeds less than1.67 Hz.This study confirmed that the periodicity of outer ringflaking could be captured at speeds as low as 0.17 Hz.Recently,other studies investigated AE and its use for the condition monitor-ing of low speed shafts and thrust ball bearings 19,20.Thesestudies demonstrated the ability to use AE to detect crack initiationand growth and also mainly focused on the investigation of AEsourcelocation.Moreover,theaforementionedexperimentfocused on a single shaft speed of 1.2 Hz and tested bearing condi-tion under loading and starved lubricating conditions.Nonetheless,the aforementioned high frequency AE signals areaccompanied by high sampling rates.Moreover,for low speedbearing fault diagnostic applications,data acquisitions need to berelatively long to capture the mechanical defect frequencies.Thecombination of high sampling rates with long data samples limitsthe feasibility of practical application of AE based approaches.Recently,it has been shown that by using a heterodyne circuit todownshift the frequency range of an AE sensor,a low cost dataacquisition(DAQ)system can be utilized to sample AE sensors ata rate comparable to vibration based techniques.This approachhas been successfully applied for gear analysis 2123 as well asfor an additive manufacturing application 24.Additionally,withthe combination of a time synchronous resampling based spectralaverage approach,it has been shown to be effective to diagnose allfour bearing fault types for shaft speeds of 3060 Hz 25,26.How-ever,the time domain signal and statistical feature combinationthat was found to be effective for the aforementioned high speedapplications were not effective for the low speed data used in thisstudy.In this paper,an approach that facilitates the use of lowsampled AE signals is presented which make the use of continuousAE time signals manageable.It was found that the combination of anew analysis signal and different condition indicators were effec-tive for the evaluated low shaft speeds.Thus,the aforementionedheterodyne analog circuit is used in combination with this new sig-nal processing approach to process AE data acquired at a low sam-pling rate,and diagnose all four bearing fault types at shaft speedsfrom 210 Hz.The diagnosis of all four bearing fault types at thepresented shaft speeds has not been presented in literature.That,in combination with the low sampling rate provides merit for thepossibility of a practical implementation of an AE based bearingmonitoring approach in industry.The remainder of the paper is structured as follows.Section 2provides a detailed explanation of the methodology.In Section 3,the details of the seeded fault tests and experimental setup usedto validate the methodology are discussed.Section 4 presents thebearing fault diagnosis results from the seeded fault tests and Sec-tion 5 concludes the paper.2.The methodologyFig.1 depicts an overview of the presented methodology.First,aheterodyne based frequency reduction technique is used to samplean AE signal at a rate comparable to vibration based approacheswith the simultaneous acquisition of a tachometer signal.Second,the sampled AE signal is time synchronously resampled using thetachometer signals zero crossing time stamp.Next,the resampledsignal is spectrally averaged and used to compute CIs for bearingfault diagnosis.The methodology will be presented in 4 sections.Section 2.1discusses the heterodyne based AE signal sampling technique.36B.Van Hecke et al./Applied Acoustics 105(2016)3544Then,in Section 2.2 a discussion on the bearing fundamental defectfrequencies is covered.It is followed by a review of time syn-chronous average(TSA),time synchronous resampling(TSR),andthe spectral averaging approach in Section 2.3.Finally,the calcula-tion of condition indicators for bearing fault diagnosis is explainedin Section 2.4.2.1.The sampling frequency reduction technique using heterodyneOne shortcoming of AE centered techniques is the substantialcomputational burden.Because the frequency of the AE sensor out-put signal is typically as high as several MHz,AE based approachesare ordinarily accompanied with sampling rates as high as severalto 10 MHz.In this paper,a sampling frequency reduction techniqueis utilized to down shift the energy related to the signal so that asampling rate comparable to vibration methods can be utilized.This type of approach has effectively been utilized for gear analysis2123,additive manufacturing monitoring 24,and for the diag-nosis of bearing faults at shaft speeds of 30 Hz and above 25,26.This approach is computationally substantial because less dataneeds to be collected and stored on the computer and ultimatelyreduces the cost accompanied with data acquisition.The concept of heterodyne has long been employed in the com-munications field.In radio,the frequency of the carrier signal of atypical amplitude modulated signal is often as high as several MHzwhereas the audio signal modulated to that carrier signal often hasa frequency as low as a few kHz.With demodulation,the ampli-tude modulated signal frequency is reduced which allows theaudio to be acquired at a much lower rate.The result is not onlya reduction in sampling rate,but also the required computationalpower for data to be processed.The AE signal demodulator used in this paper works similarly toa radio quadrature demodulator:shifting the carrier frequency tobaseband,followed by low pass filtering.The approach appliedhere is called heterodyne.Mathematically,heterodyning is basedon a trigonometric identity.For two signals with frequency f1and f2,respectively,it could be written as the following:sin 2pf1tsin 2pf2t 12cos 2pf1?f2t?12cos2pf1 f2t?1where f1is the AE carrier frequency and f2is the demodulators ref-erence signal frequency.Forinstance,letf1 3 Hzandf2 4 Hz,andnotey1 sin2p3tandy2 sin2p4t:Then,theirmultiplicationY y1y2,is presented in Fig.2.Then,as expressed in Fig.3,the modulated signal is low pass fil-tered to reject the high frequency image at frequency(f1 f2).A detailed dialogue of the heterodyne approach applied on theraw AE signal is provided next.It is generally accepted that ampli-tude modulation is the major form of modulation for AE signals.Although,frequency and phase modulation are potentially existentin the AE signal,they are considered trivial and will not be dis-cussed herein.The amplitude modulation function is provided by(2).Ua Um mxcosxct2where Uais the modulated signal,Umis the carrier signal amplitude,xcis the carrier signal frequency,m is the modulation coefficient,and x is the signal of interest.With an amplitude Xmand frequencyX,assume that x can be expressed as:x XmcosXt3Note that it is presumed that the frequencyxof the signal x isnormally much smaller than the frequencyxcof the carrier signal.Then,with the heterodyne technique,the modulated signal will bemultiplied by a unit amplitude reference signal cosxct.The resultUois provided next:Uo Um mxcosxctcosxct Um mx1212cos2xct?4Next,after substituting Eq.(3)into Eq.(4):Uo12Um12mXmcosXt 12Umcos2xct14mXmcos2xcXt cos2xc?Xt?5Because Umdoes not enclose any useful information associatedwith the modulated signal,it is set as 0,or removed via de-trending.From(5),it can be understood that only the part relatedto the signal of interest,12mXmcosXt,will remain after low pass fil-tering,while the high frequency associated components aroundfrequency 2xcwill be removed.The addition of the demodulation step achieves the purpose ofdown shifting the signal frequency to 10 s of kHz,which is close tothe frequency range of vibration signals.Hence,any data acquisi-tion board with a low sampling rate should be able to samplepre-processed AE data.2.2.Discussion on the bearing fundamental defect frequenciesAs a bearing rotates at a constant speed,its AE signal can be the-oretically characterized by a periodical property.Generally,thereare 4 fundamental defect frequencies to describe this motion.The 4 defect frequencies are:fundamental train frequency(FTF),ball spin frequency(BSF),ball pass frequency outer(BPFO),and ballpass frequency inner(BPFI).These frequencies respectively repre-sent the defect frequencies of the cage,ball,outer race,and innerrace 27.The defect frequencies are defined as:FTF x=2 1?De=Dpcos?6BSF xDp=2Def1?De=Dpcos?2g7BPFO xZ=2 1?De=Dpcos?8BPFI xZ=2 1 De=Dpcos?9where Deis the rolling elements diameter,Dpis the pitch diameter,Z is the number of rolling elements,is the contact angle indegrees,andxis the rotational speed of the shaft in Hz.The draw-ing of a 6205-2RS steel ball bearing is shown in Fig.4.Moreover,theparameters and the calculated defect frequency multipliers of the6205-2RS are provided in Tables 1 and 2 respectively.At a given shaft speed,the frequency spectra of the monitoredbearing should theoretically contain peaks that are related to thepresence or absence of a bearing defect frequency.These peaksare often difficult to observe due to mechanical noise present inthe signal.Thus,signal processing techniques,such as averagingFig.1.Overview of the methodology.B.Van Hecke et al./Applied Acoustics 105(2016)354437approaches,have been developed to help reduce such noise andincrease signal to noise ratio.Measuring the bearing defect fre-quencies shown in Eqs.(6)(9)are usually utilized by narrowbandand sideband based analysis methods.Because the amplitude ofthose bearing defect frequencies are very small compared to shaftorder and gear mesh frequency,detecting bearing faults by directreading from narrowband using Fourier analysis is difficult.Toovercome the preceding drawback of the conventional narrowbandanalysis,bearing envelope analysis(BEA)has been developed.Although the BEA method is well established,the selection of aFig.2.The multiplication of two sinusoid signals.Fig.3.Extracting the heterodyned signal by frequency domain filtering.38B.Van Hecke et al./Applied Acoustics 105(2016)3544proper envelope demodulation band(e.g.system resonance fre-quency)is either hidden or very complicated.An improper windowselection could compromise the diagnostic performance accordingto the recent BEA papers 28,29.The method presented in thispaper is different from those direct bearing frequency readingbased method.By applying Welchs spectral averaging methodand extracting fault features as condition indicators,faulty bearingsignals become statistically separable and those trends are main-tained although the operating parameter is altered(e.g.shaftspeed).In the following section,the averaging approach employedin this paper is discussed.2.3.Spectral averaging of AE signalTime synchronous averaging(TSA)is a validated approach forthe extraction of periodic waveforms and has multiple appli