Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform

Abstract Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform such that fault feature adaptability is enabled. Within ESW, a parametric optimization is performed on the measured signal to obtain a quality TQWT basis that best demonstrate the hidden fault feature. TQWT is introduced as it provides a vast wavelet dictionary with time-frequency localization ability. The parametric optimization is guided according to the maximization of fault feature ratio, which is a new quantitative measure of periodic fault signatures. The fault feature ratio is derived from the digital Hilbert demodulation analysis with an insightful quantitative interpretation. The output of ESW on the measured signal is a selected wavelet scale with indicated fault features. It is verified via numerical simulations that ESW can match the oscillatory behavior of signals without artificially specified. The proposed method is applied to two engineering cases, signals of which were collected from wind turbine and steel temper mill, to verify its effectiveness. The processed results demonstrate that the proposed method is more effective in extracting weak fault features of induction motor bearings compared with Fourier transform, direct Hilbert envelope spectrum, different wavelet transforms and spectral kurtosis.

[1]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[2]  Naim Baydar,et al.  A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. , 2001 .

[3]  Antero Arkkio,et al.  DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors , 2007 .

[4]  Ruqiang Yan,et al.  Harmonic wavelet-based data filtering for enhanced machine defect identification , 2010 .

[5]  Yi Qin,et al.  Research on iterated Hilbert transform and its application in mechanical fault diagnosis , 2008 .

[6]  H. Khalil,et al.  Wavelet-based methods for the prognosis of mechanical and electrical failures in electric motors , 2005 .

[7]  L. Eren,et al.  Detecting motor bearing faults , 2004, IEEE Instrumentation & Measurement Magazine.

[8]  Ivan W. Selesnick,et al.  Sparse signal representations using the tunable Q-factor wavelet transform , 2011, Optical Engineering + Applications.

[9]  Bing Li,et al.  Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors , 2012 .

[10]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[11]  Jin Chen,et al.  Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum , 2011 .

[12]  Luis Romeral,et al.  Wavelet and PDD as fault detection techniques , 2010 .

[13]  Ivan W. Selesnick,et al.  Frequency-Domain Design of Overcomplete Rational-Dilation Wavelet Transforms , 2009, IEEE Transactions on Signal Processing.

[14]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[15]  K. I. Ramachandran,et al.  Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN) , 2010, Expert Syst. Appl..

[16]  Javad Poshtan,et al.  Bearing fault detection using wavelet packet transform of induction motor stator current , 2007 .

[17]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[18]  Pragasen Pillay,et al.  Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform , 2012 .

[19]  Y. Zi,et al.  Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis , 2009 .

[20]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[21]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[22]  Jafar Zarei,et al.  Induction motors bearing fault detection using pattern recognition techniques , 2012, Expert Syst. Appl..

[23]  Robert M. Parkin,et al.  On the energy leakage of discrete wavelet transform , 2009 .

[24]  Gary G. Yen,et al.  Wavelet packet feature extraction for vibration monitoring , 2000, IEEE Trans. Ind. Electron..

[25]  Jian-Da Wu,et al.  Investigation of engine fault diagnosis using discrete wavelet transform and neural network , 2008, Expert Syst. Appl..

[26]  J. Antoni.  Cyclostationarity by examples , 2009 .

[27]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[28]  Murat Alper Basaran,et al.  Condition monitoring of speed controlled induction motors using wavelet packets and discriminant analysis , 2011, Expert Syst. Appl..

[29]  Ming J. Zuo,et al.  GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER , 2003 .

[30]  Peter W. Tse,et al.  Use of autocorrelation of wavelet coefficients for fault diagnosis , 2009 .

[31]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[32]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[33]  B. Tang,et al.  Higher-density dyadic wavelet transform and its application , 2010 .

[34]  S. Gopalakrishnan,et al.  Detection of stator short circuits in VSI-fed brushless DC motors using wavelet transform , 2006, IEEE Transactions on Energy Conversion.

[35]  Antero Arkkio,et al.  Detection of combined faults in induction machines with stator parallel branches through the DWT of the startup current , 2009 .

[36]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[37]  Shuai Wang,et al.  Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis , 2013 .

[38]  Ivan W. Selesnick,et al.  Wavelet Transform With Tunable Q-Factor , 2011, IEEE Transactions on Signal Processing.

[39]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[40]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[41]  Yaguo Lei,et al.  Fast-varying AM-FM components extraction based on an adaptive STFT , 2012, Digit. Signal Process..

[42]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

[43]  Ming Liang,et al.  A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform , 2013 .

[44]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[45]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .

[46]  Nagarajan Murali,et al.  Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference , 2013, IEEE Transactions on Industrial Electronics.