基于第二代小波变换的滚动轴承故障诊断方法与实验研究含程序
收藏
资源目录
压缩包内文档预览：
编号：91865336
类型：共享资源
大小：7.75MB
格式：ZIP
上传时间：2020-08-10
上传人：QQ14****9609
认证信息
个人认证
郭**（实名认证）
陕西
IP属地：陕西
40
积分
- 关 键 词：
-
基于
第二代
变换
滚动轴承
故障诊断
方法
实验
研究
程序
- 资源描述：
-
基于第二代小波变换的滚动轴承故障诊断方法与实验研究含程序,基于,第二代,变换,滚动轴承,故障诊断,方法,实验,研究,程序
- 内容简介：
-
Mechanical SystemsandSignal ProcessingMechanical Systems and Signal Processing 22 (2008) 542556Rotating machinery fault diagnosis usingsignal-adapted lifting schemeZhen Lia,?, Zhengjia Heb, Yanyang Zia, Hongkai JiangaaSchool of Mechanical Engineering, Xian Jiaotong University, 710049 Xian, ChinabState Key Lab for Manufacturing Systems Engineering, Xian Jiaotong University, 710049 Xian, ChinaReceived 24 March 2006; received in revised form 25 September 2007; accepted 26 September 2007Available online 2 October 2007AbstractWavelet transform has been widely used for vibration-based machine fault diagnosis. However, it is a difficult taskto choose or design appropriate wavelet or wavelets for a given application. In this paper, a new signal-adapted liftingscheme for rotating machinery fault diagnosis is proposed, which allows us to construct a wavelet directly from thestatistics of a given signal. The prediction operator based on genetic algorithms is designed to maximize the kurtosis ofdetail signal produced by the lifting scheme, and the update operator is designed to minimize a reconstruction error. Thesignal-adapted lifting scheme is applied to analyze bearing and gearbox vibration signals. The conventional diagnosistechniques and non-adaptive lifting scheme are also used to analyze the same signals for comparison. The resultsdemonstrate that the signal-adapted lifting scheme is more effective in extracting inherent fault features from complexvibration signals.r 2007 Elsevier Ltd. All rights reserved.Keywords: Adaptive lifting scheme; Fault diagnosis; Vibration signal analysis1. IntroductionRotating machinery is very popular in industrial applications. An unexpected failure of rotating machinerymay result in significant economic losses. In order to avoid the occurrence of abnormal events, vibration signalanalysis is widely used in the rotating machinery condition monitoring and fault diagnosis. Usually, dependingon machine operating conditions and severity of defects, the measured vibration signals are always complexand non-stationary, and the useful fault information is buried in noises. Therefore it is difficult to detect thesymptoms of a potential failure from such vibration signals.There are many vibration-based diagnosis techniques available for rotating machinery 1. The Hilbertenvelope analysis 2 has been used successfully in rotating machinery fault diagnosis as one of the mostcommon demodulation methods. Unfortunately, the central frequency of filter is determined by experiencewhile forming an envelope signal, which will cause great influence on the results. Cepstrum analysis 3ARTICLE IN PRESS/locate/jnlabr/ymssp0888-3270/$-see front matter r 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.ymssp.2007.09.008?Corresponding author. Tel.: +862982667963; fax: +862982663689.E-mail address: lizhen (Z. Li).was developed to identify the sideband components and extract fault features of gearbox. However,if the signal-to-noise ratio (SNR) is very low and the fault signature is buried in strong background noises,the sideband components are not easy to be isolated using the cepstrum analysis. In the time domain,time domain averaging 4 may be one of the most popular traditional techniques for detecting gear-box faults; it is powerful in suppressing the noise and other non-synchronous components. However,this technique has a limitation that it usually requires a reference signal to obtain a synchronously averagedsignal.Wavelet transform is well known for its ability to focus on localized structures in timefrequencydomain 5. In the past few years, many researchers have investigated the application of wavelet transformto vibration signals analysis for fault diagnosis of rotating machinery. For instance, Wang andMcFadden 6 used orthogonal wavelets such as the Db4 and harmonic wavelet to detect the early gearfailure. Lin and Qu 7 presented a denoising method based on Morlet wavelet to extract fault features ofgearbox. Sun et al. 8 applied singularity analysis through the continuous wavelet transform (CWT) tocapture the time of impacts in bearing vibration signals. Li and Ma 9 used continuous wavelet analysis todetect localized bearing defects based on vibration signals. Tse et al. 10 performed a comparison on theeffectiveness of envelope detection and CWT in fault diagnosis of roller bearings. These wavelet techniques areeffective for fault detection of rotating machinery. However, one common approach taken by the wavelettechniques mentioned above is that the standard wavelets selected from a library of previously designedwavelet functions are used as the mother wavelets. Unfortunately, such standard wavelet functions areindependent of a given signal. Since different types of wavelets have different timefrequency structures, it isalways very difficult to choose the best wavelet function for extracting fault features from given signal.Moreover, an inappropriate wavelet will reduce the accuracy of the fault detection. To overcome the abovelimitations, it is necessary to develop new methods to design signal-adapted wavelet functions for rotatingmachinery fault diagnosis.The lifting scheme is introduced by Sweldens as a powerful tool to construct biorthogonal wavelets in thespatial domain 11,12. It provides a great deal of flexibility and freedom for the construction of biorthogonalwavelets, and can be used to construct adaptive wavelets by the design of prediction operator and updateoperator.In this paper, we present a new signal-adapted lifting scheme for rotating machinery fault diagnosis. In theprediction step, kurtosis is used as the performance measurement of the prediction operator, and geneticalgorithms are employed to design prediction operator in order to maximize the kurtosis of detail signals. Inthe update step, the update operator is designed to minimize the difference between the original andreconstructed signals when the high-frequency signal is removed. We find that the signal-adapted liftingscheme can closely match the characteristics of vibration signals, and it is very effective to extract the transientfeature components from the complex vibration signals.The structure of the paper is organized as follows. In Section 2, the theory of lifting scheme isreviewed briefly. In Section 3, a new signal-adapted lifting scheme is set up. The constraints of thedesigned lifting scheme are described. The genetic algorithms are used to design the prediction operatorbased on kurtosis maximization principle. The update operator is designed to minimize the reconstructionerror. In Section 4, we discuss the redundant version of the lifting scheme. In Section 5, the signal-adaptedlifting scheme is applied to detect localized defects of roller bearing and gearbox. Comparisons withconventional diagnosis techniques and non-adaptive lifting scheme are also shown. Conclusions are given inSection 6.2. The lifting schemeThe lifting scheme is a spatial domain construction of biorthogonal wavelets. It does not rely on the Fouriertransform. The lifting scheme consists in three main steps 11,12.In the split step, the original signal x xii2Zis split into even samples s0 s0ii2Zand odd samplesd0 d0ii2Z,s0i x2i;d0i x2i1.(1)ARTICLE IN PRESSZ. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556543In the prediction step, we apply an operator P on s(0)to predict d(0). The prediction error d dii2Zisregarded as the detail signal of x,di d0i?XN=2r?N=21prs0ir,(2)where prare coefficients of the prediction operator P and N is the number of coefficients of predictionoperator.In the update step, an update of even samples s(0)is accomplished by using an update operator U on detailsignal d and adding the result to s(0). The update sequence s sii2Zcan be regarded as the approximationsignal of x,si s0iXN=2j?N=21ujdij?1,(3)where ujare coefficients of the update operator U andN is the number of coefficients of update operator.Using approximation signal s again as the input to lifting scheme can generate detail signal andapproximation signal at lower resolution level.3. The design of signal-adapted lifting scheme3.1. Constraints of the designed lifting schemeThe theorems in 12, expressed by Sweldens in the lifting framework, ensure the biorthogonality of the filterbank relevant to the lifting scheme. However, different prediction operator P and update operator U canconstruct wavelet functions with different timefrequency structures. In order to optimize the predictionoperator and update operator, Gouze et al. 13 introduced two meaningful conditions for operators P and U.The symmetrical linear phase constraints are expressed as follows:pr p?r1;r 1;2;.;N=2,(4)uj u?j1j 1;2;.;N=2.(5)The filtering normalization constraints are expressed as below:XN=2r1pr12,(6)XN=2j1uj14.(7)3.2. Design of the prediction operator PThe prediction step provides the detail signal d. To ensure that the derived lifting filters can effectivelyisolate feature components from the original signal, a criterion for the prediction operator is needed. Kurtosisis used in engineering to detect fault symptoms because it is sensitive to sharp changed structures, such asimpulses 3. In this paper, we use kurtosis as the performance measurement of prediction operator. Thecriterion of prediction operator P is defined as follows:KPEfd ?d4gs4,(8)ARTICLE IN PRESSZ. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556544whered and s are the mean and standard deviation of detail signal d, and E? is the expectation. Ourobjective is to find an optimal prediction operator Poptthat maximizes the kurtosis criterion KPwhilesatisfying the constraints Eqs. (4) and (6).Many optimization methods have been presented and each has its own advantages and limitations. Theflexible polyhedron method 14 involves substantive computation. Neural networks 15 can be used to findglobal optimization, but the selection of the architecture for neural networks is a tedious task. From theoptimization point of view, one of the main advantages of genetic algorithms 16 is that it does not havemathematical requirements on the optimization problem. Moreover, genetic algorithms are effective in globaloptimization. Therefore, in this paper, genetic algorithms are used to optimize the prediction operator P.In the initialization population for prediction operator P, the coefficients (p?N/2+1,y,pN/2) of predictionoperator P are coded using the real-coded mechanism. First, the coefficients (p2,y,pN/2) are generatedrandomly. Second, the coefficients (p?N/2+1,y,p?1) are given according to Eq. (4). Finally, the coefficients(p0, p1) are obtained by the following formula:p0 p112?XN=2r2pr.(9)The arithmetic crossover and uniform mutation operators commonly used in genetic algorithms are employedfor the optimization process 16. To increase the efficiency of the process, the population scale is set to 50, thenumber of iteration to 100, the probability of crossover to 0.7 and the probability of mutation to 0.025.3.3. Design of the update operator UAn effective update operator produces approximation signal, offering an accurate representation of theoriginal signal at the lower resolution. To obtain the optimal update coefficients, the quadratic error ofreconstruction is used as the criterion for the update operator 13. It is described as follows:JU Ef s0? s02g Efd0? d02g,(10)where s0andd0represent even samples and odd samples of the reconstructed signal x without using thedetail signal d. When d 0, the inverse lifting scheme is shown in Fig. 1.From Fig. 1, s0andd0are given by the following equations: s0 s,(11)d0 Pns.(12)During the design of the update operator, our goal is to find the optimal update operator Uoptthat couldminimize the criterion JUwhile satisfying the constraints (5) and (7).Let l be the Lagrange operator, under the constraint (7), the new criterion can be expressed as follows:JUu;l Ef s0? s02g Efd0? d02g l14?XN=2j1uj01A.(13)ARTICLE IN PRESS (0)s -UP MERGEd = 0sx d(0)(0)+Fig. 1. The inverse lifting scheme with d 0.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556545Considering Eqs. (3), (5), and (11), we obtainEf s0? s02g EXN=2j1ujdij?1 di?j243528:9=;.(14)From Eqs. (2), (3), (5), and (12), we deduce the following result:Efd0? d02g EXN=2j1ujs0ij s0i?j1 ? di243528:9=;,(15)wheres0iXN=2r1prdi?r dir?1.(16)To minimize Eq. (13), partial derivatives of the criterion JU(u,l) with respect to the variables ujand l areexpressed as follows:qJUu;lquj 0;j 1;2;.;N=2,(17)qJUu;lql 0.(18)By settingAk;l Efdik?1 di?kdil?1 di?l s0ik s0i?k1s0il s0i?l1g,(19)Bk;1 Efdis0ik s0i?k1g,(20)a linear system with (N=2 1) variables, which is combined by Eqs. (17) and (18), can be rewritten asAX B(21)withA A1;1?A1;N=2?1.AN=2;1?AN=2;N=2?11?1026666643777775,(22)X u1;.;uN=2;l?T,(23)B B1;1;.;BN=2;1;1=4?T.(24)According to Eq. (5) and the vector X u1;u2;.;uN=2;l?, we can obtain the optimal update operatorUopt u?N=21;.;u1;.;uN=2?.Because longer time is consumed if the larger values are chosen for the number of prediction coefficients andupdate coefficients, the number of prediction operator should be chosen from 6, 8, and 10, and the number ofupdate operator should be selected from 4, 6 and 8.4. Redundant lifting schemeFor the classical wavelet transform, a solution for translation invariance is given by redundant wavelettransform, which eliminates the decimation step and retains the information of low- and high-frequencysignals. The redundant wavelet transform can also be translated into a redundant lifting scheme 17.ARTICLE IN PRESSZ. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556546Based on the design of signal-adapted lifting scheme mentioned in Section 3, we obtain the predictionoperator P and update operator U which closely match the inspected signal. In the redundant lifting scheme,instead of partitioning a signal x into s(0)and d(0), we let both s(0)and d(0)be x. The redundant predictionoperator P(k)and the redundant update operator U(k)are computed by padding the prediction operator P andupdate operator U with zeros at the corresponding level k. The redundant decomposition results of anapproximation signal s(k)at level k with lifting scheme are expressed by following equations:dk1 sk? Pksk,(25)sk1 sk Ukdk1,(26)where d(k+1)and s(k+1)are detail signal and approximation signal at level k+1.5. Application of signal-adapted lifting scheme in rotating machinery fault diagnosisTo demonstrate the performance of signal-adapted lifting scheme in rotating machinery fault diagnosis, thissection presents two application examples for the detection of localized defects in rolling bearing and gearbox.5.1. Detection of the outer-race defect in rolling bearingWe use the signal-adapted lifting scheme to identify localized defects on the outer raceway of rollingbearing. The geometric parameters of the tested bearing in the experiment are listed in Table 1. Fig. 2 showsthe outer-race of bearing with localized defects. The vibration signals are picked up at a constant inner-racerotation speed of 385rpm. Based on the geometric parameters and rotating speed, the characteristic frequencycan be calculated 3. The frequency of ball-passing-outer-raceway (fbpo) is 46.30Hz.The signals are digitized at a sampling frequency of 25.6KHz. Fig. 3(a) shows the vibration signal of theinspected bearing. Its FFT spectrum on a log amplitude scale is given in Fig. 3(b). According to bearingkinematics and dynamics, impact occurs each time when a roller encounters the spalls. However, fromARTICLE IN PRESSTable 1The geometric parameters of the tested bearingBall diameter68mmPitch diameter450mmContact angle01Number of rolling elements17Fig. 2. The outer-race of bearing with the localized defects.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556547Fig. 3(a), these impacts are buried in the wideband noise and environmental noise. The meaningfulinformation for detecting the failure is not given in Fig. 3(b).In order to extract the feature components caused by outer-raceway defects, we use the signal-adapted liftingscheme to analyze bearing vibration signal shown in Fig. 3(a). The number of prediction coefficients is 8, and thenumber of update coefficients is 4. Using the signal-adapted lifting scheme introduced in Section 3, the predictionoperator and update operator are calculated, which are adapted to the vibration signal shown in Fig. 3(a). Theprediction operator is 0.1302, ?0.0934, ?0.0159, 0.4791, 0.4791, ?0.0159, ?0.0934, 0.1302, and the updateoperator is 0.1306, 0.1194, 0.1194, 0.1306. Fig. 4 shows the redundant decomposition results. Evenly spacedimpulse clusters can be observed from the detail signal (d1). The periodic intervals of the impulse clusters areapproximately equal to 21.7ms, which is equivalent to the inverse of the frequency of ball-passing-outer-raceway(fbpo). Hence it can be concluded that the impulses are caused by the outer-raceway defect of bearing.To demonstrate the efficiency of the signal-adapted lifting scheme, the same vibration signal is analyzed byusing Hilbert envelope analysis and non-adaptive lifting scheme. Fig. 5(a) shows the band-pass filtered signal(the pass band is 50007000Hz). Its envelope spectrum is shown in Fig. 5(b). From Fig. 5(b), there is not anobvious spectrum line at the characteristic frequency of outer-race defect (46.30Hz).The redundant decomposition results using non-adaptive lifting scheme are shown in Fig. 6, and theapproximation signals (a1) and detail signals (d1) of the vibration signal are also given. In the non-adaptivelifting scheme, the prediction operator and update operator, which are independent of the inspected signal, areobtained by interpolation subdivision method introduced in 18. They are ?0.0024, 0.0239, ?0.1196, 0.5981,0.5981, ?0.1196, 0.0239, ?0.0024 and ?0.0313, 0.2813, 0.2813, ?0.0313, respectively. In Fig. 6, only severalsharp impulses appear in the detail signals (d1). So it is difficult to draw any conclusive result from Fig. 6.5.2. Detection of gearbox defectsIn the second example, we use the signal-adapted lifting scheme to analyze gearbox vibration signals in amine hoist system for detecting inherent faults during the operation of the machine. The diagram of the minehoist system is illustrated in Fig. 7.ARTICLE IN PRESSFig. 3. (a) The vibration signal collected from the bearing with localized defect on the outer raceway and (b) its log-amplitude spectrum.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556548The rotating speed of the motor is 495rpm. The transmission ratio of the gearbox is 0.132. The rotatingfrequency of gear #1 is 8.25Hz. The rotating frequency of gear #2 is 1.09Hz. For gear transmission, themeshing frequency of gear is calculated byfz nz=60,(27)ARTICLE IN PRESSFig. 4. The redundant composition result of the signal in Fig. 3(a) using the signal-adapted lifting scheme.Fig. 5. The results of bearing vibration signal using Hilbert envelope analysis: (a) the band-pass filtered signal (the pass band is50007000Hz) and (b) its envelope spectrum.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556549where n is the rotating speed of the input shaft, z is the number of the gear teeth. In our gearbox, n 495rpmand z 20. It follows from Eq. (27) that the meshing frequency of gearbox is 165Hz.The vibration signal is acquired by an accelerometer located on the input shaft side of the gearbox. Thesampling frequency is 2000Hz, and the number of samples is 16K (1K 1024). The vibration signal of theinspected gearbox is shown in Fig. 8. The waveform of vibration signal is very complex and the useful faultinformation is hidden in the vibration signal.The power density spectrum of the vibration signal is illustrated in Fig. 9. There exist three frequencies of165, 330 and 495Hz, and 8.25Hz sidebands around them. Considering the shaft speed and the number of thegear teeth, it can be concluded that the three frequencies correspond to the gear meshing frequency and itsharmonics, respectively. The 8.25Hz is just equal to the rotating frequency of gear #1. It indicates that themodulation fault corresponding to the rotating frequency of gear #1 appears in the gearbox. Although thespectrum analysis provides useful information of the described system, but classical Fourier analysis is basedon the assumption that the vibration signals are stationary. It is ineffective to extract inherent faultcomponents produced by the presence of localized defects such as tooth crack in gear systems.ARTICLE IN PRESSFig. 6. The redundant composition result of the signal in Fig. 3(a) using the non-adaptive lifting scheme.Fig. 7. The diagram of the mine hoist system.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556550The vibration signal shown in Fig. 8 is analyzed using the cepstrum analysis. The result is shown inFig. 10.There exists an evident spike at the rotating period of gear #1(0.121s). The cepstrum analysis givesuseful information of the described system, but we cannot distinguish simultaneously both localized anddistributed faults of gear #1 from the result of the cepstrum analysis.To isolate the feature components from the complex vibration signal, we use the signal-adapted liftingscheme to analyze the vibration signal shown in Fig. 8. The number of prediction coefficients is 8, and thenumber of update coefficients is 4. According to the optimization algorithms of prediction operator andupdate operator in Section 3, we obtain the prediction and update operators, which are adapted to thevibration signal shown in Fig. 8. The prediction operator is ?0.3041, ?0.7379, 0.9395, 0.6025, 0.6025, 0.9395,?0.7379, ?0.3041. The update operator is 0.0215, 0.2285, 0.2285, 0.0215. The length of approximationsignal and detail signal at all levels is the same as the original signal, which is 8.2s. In order to identify the faultinformation by visual inspection, the decomposition results in 01s are displayed. The detail signal (01s) atlevel 1 of redundant composition results using the signal-adapted lifting scheme is shown in Fig. 11. FromFig. 11, periodic impulses are obvious in the detail signal. The period is just about 0.121s, which is inaccordance with the rotating frequency of gear #1. It indicates that localized fault appears in the gear #1. Theapproximation signal (01s) at level 2 is shown in Fig. 12. The amplitude modulation is clearly revealed in theapproximation signal. In order to confirm the gearbox fault, we use Hilbert envelope analysis to demodulatethe approximation signal. The Hilbert envelope spectrum is shown in Fig. 13. There are three dominant peaksin the envelope spectrum which occur at the frequencies of 8.25, 16.5 and 33Hz. They correspond to therotating frequency of gear #1, its second and fourth harmonics, respectively. So we can conclude thatthe modulation frequency of gearbox fault is the rotating frequency of gear #1. By studying the structure ofthe gearbox in Fig. 7, the amplitude modulation is caused by improper assembly and parallel misalignment.After overhaul, the modulation fault is corrected by adjusting the parallel alignment of the gears and thevibration of the described system decreases markedly. The vibration signal is shown in Fig. 14, and itscepstrum is illustrated in Fig. 15. The amplitude of the spike at the rotating period of gear #1 (0.121s) is farlower than that in Fig. 10.ARTICLE IN PRESSFig. 8. Vibration signal of the inspected gearbox.Fig. 9. Power density spectrum of the signal shown in Fig. 8.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556551ARTICLE IN PRESSFig. 10. The cepstrum of the signal shown in Fig. 8.Fig. 11. The detail signal (01s) at level 1 of the signal in Fig. 8 using the signal-adapted lifting scheme.Fig. 12. The approximation signal (01s) at level 2 of the signal in Fig. 8 using the signal-adapted lifting scheme.Fig. 13. The envelope spectrum of the approximation signal at the frequency range 080Hz.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556552ARTICLE IN PRESSFig. 14. The vibration signal of gearbox after overhaul.Fig. 15. The cepstrum of the signal shown in Fig. 14.Fig. 16. The decomposition results (01s) of the signal in Fig. 14 using the signal-adapted lifting scheme. (a) The detail signal at level 1and (b) the approximation signal at level 2.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556553We use the signal-adapted lifting scheme to analyze the signal shown in Fig. 14. The detail signal at level 1and the approximation signal at level 2 are shown in Fig. 16(a) and (b), respectively. From Fig. 16(a), theperiodic impulses caused by the localized fault of gear #1 still exist, but the amplitude modulation disappearsARTICLE IN PRESSFig. 17. The decomposition results (01s) of the signal in Fig. 8 using the non-adaptive lifting scheme.Fig. 18. The result of the signal in Fig. 8 using the narrowband demodulation: (a) the filtered signal around the second harmonic ofmeshing frequency, (b) the demodulated waveform using Hilbert transform.Z. Li et al. / Mechanical Systems and Signal Processing 22 (2008) 542556554in Fig. 16(b). Therefore, the signal-adapted lifting scheme can be used to distinguish simultaneously bothlocalized and distributed faults for gearbox fault diagnosis.To confirm the effectiveness of the signal-adapted lifting scheme in gearbox fault diagnosis, the vibrationsignal shown in Fig. 8 is analyzed by non-adaptive lifting scheme. Fig. 17 shows the decomposition resultsusing non-adaptive lifting scheme. The amplitude modulation can be observed from the detail signal d2inFig. 17, but the periodic impulses caused by the localized fault of gear #1 are not extracted effectively inFig. 17. The narrowband demodulation technique 19 is employed around the second harmonic of meshingfrequency for comparison, and the bandwidth used in the demodulation is 80Hz. The filtered signal is shownin Fig. 18(a), and its demodulated waveform using Hilbert transform is given in Fig. 18(b). The amplitudemodulation is clearly revealed in Fig. 18(a). The localized fault of gear #1 is not extracted effectively in Fig. 18.6. ConclusionsIn this paper, we have proposed a new signal-adapted lifting scheme for fault diagnosis of rotatingmachinery. The prediction operator based on the kurtosis maximization principle is designed by using geneticalgorithms. The update operator is designed to minimize the reconstruction error. The signal-adapted liftingscheme can closely match the characteristics of the inspected vibration signals. The proposed method is testedin the defect detection of rolling bearing and gearbox. The results show that it is more effective for extractingthe fault features than conventional diagnosis techniques and non-adaptive lifting scheme. Therefore thesignal-adapted lifting scheme provides an effective method to reveal the fault feature components from thevibration signals for rotating machinery fault diagnosis.AcknowledgmentsThis work was supported by the key project of National Natural Science Foundation of China (No.50335030), National High-tech R&D Program of China (863 Program) (2006AA04Z430), National BasicResearch Program of China (No. 2005CB72
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
川公网安备: 51019002004831号