Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis.

[1]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

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

[3]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

[4]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[5]  Yuh-Tay Sheen,et al.  Constructing a wavelet-based envelope function for vibration signal analysis , 2004 .

[6]  Jie Liu,et al.  An Extended Wavelet Spectrum for Bearing Fault Diagnostics , 2008, IEEE Transactions on Instrumentation and Measurement.

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

[8]  Yuh-Tay Sheen,et al.  A complex filter for vibration signal demodulation in bearing defect diagnosis , 2004 .

[9]  Wei He,et al.  Bearing fault detection based on optimal wavelet filter and sparse code shrinkage , 2009 .

[10]  Christos Yiakopoulos,et al.  Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings , 2002 .

[11]  Jing Lin,et al.  Feature extraction of machine sound using wavelet and its application in fault diagnosis , 2001 .

[12]  Asoke K. Nandi,et al.  Genetic algorithms for feature selection in machine condition monitoring with vibration signals , 2000 .

[13]  Wenxian Yang,et al.  A contribution to the applicability of complex wavelet analysis of ultrasonic signals , 2004 .

[14]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[15]  J. Rafiee,et al.  A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system , 2009, Expert Syst. Appl..

[16]  S. Mallat.  A wavelet tour of signal processing , 1998 .

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

[18]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[19]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

[20]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[21]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[22]  Xavier Chiementin,et al.  Early Detection of Fatigue Damage on Rolling Element Bearings Using Adapted Wavelet , 2007 .

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

[24]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[25]  I. S. Bozchalooi,et al.  A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals , 2008 .

[26]  Patrick S. K. Chua,et al.  Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor , 2006 .

[27]  Gaoyong Luo,et al.  Real-Time Condition Monitoring by Significant and Natural Frequencies Analysis of Vibration Signal with Wavelet Filter and Autocorrelation Enhancement , 2000 .

[28]  Joseph Mathew,et al.  Multiple Band-Pass Autoregressive Demodulation for Rolling-Element Bearing Fault Diagnosis , 2001 .

[29]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[30]  Ioannis Antoniadis,et al.  Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets , 2002 .