Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

The fault feature of the rolling bearing is difficult to extract when weak fault occurs and interference exists. The tunable Q-factor wavelet transform (TQWT) can effectively extract the weak fault characteristic of the rolling bearing, but the manual selection of the Q-factor affects the decomposition result and only using TQWT presents interference. Aiming at the above problems, an adaptive tunable Q-factor wavelet transform (ATQWT) and spectral kurtosis (SK) method is proposed in this paper. Firstly, the method applies particle swarm optimization (PSO) to seek the optimized Q-factor for avoiding manual selection, which uses the kurtosis value of the transient impact component as the particle fitness function. The rolling bearing fault signal is decomposed into continuous oscillation component and transient impact component containing fault feature by the optimized Q-factor. Then, due to the presence of interference in the decomposition result of ATQWT, the SK analysis of the transient impact component is used to determine the frequency band of periodic impact component characterizing fault feature by fast kurtogram. Finally, the band-pass filter established through the above frequency band is employed to filter the interference in the transient impact component. Simulation and experimental results indicate that the ATQWT can highlight the periodic impact component characterizing rolling bearing fault feature, and the SK can filter interference in the transient impact component, which improves feature extraction effect and has great significance to enhance fault diagnosis accuracy of the rolling bearing. Compared with EEMD-TQWT and TQWT-SK, the fault feature extracted by the proposed method is prominent and effective.

[1]  Ke Li,et al.  An Adaptive Spectral Kurtosis Method and its Application to Fault Detection of Rolling Element Bearings , 2020, IEEE Transactions on Instrumentation and Measurement.

[2]  Ivan W. Selesnick,et al.  Resonance-based signal decomposition: A new sparsity-enabled signal analysis method , 2011, Signal Process..

[3]  Arun K. Samantaray,et al.  Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising , 2017 .

[4]  Wu Deng,et al.  An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem , 2019, IEEE Access.

[5]  Abbas Rohani Bastami,et al.  Rolling element bearing diagnosis using spectral kurtosis based on optimized impulse response wavelet , 2019, Journal of Vibration and Control.

[6]  K. Manivannan,et al.  Bearing Fault Diagnosis using Wavelet Packet Transform, Hybrid PSO and Support Vector Machine☆ , 2014 .

[7]  Hongmei Liu,et al.  Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals , 2016 .

[8]  Huimin Zhao,et al.  An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network , 2020, IEEE Transactions on Instrumentation and Measurement.

[9]  Shuting Wan,et al.  Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK , 2018 .

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

[11]  Fucai Li,et al.  Envelope Spectrum L-Kurtosis and Its Application for Fault Detection of Rolling Element Bearings , 2020, IEEE Transactions on Instrumentation and Measurement.

[12]  Huibin Lin,et al.  Rolling bearing fault diagnosis using impulse feature enhancement and nonconvex regularization , 2020 .

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

[14]  P. Srinivasa Pai,et al.  Multiscale Slope Feature Extraction for Gear and Bearing Fault Diagnosis Using Wavelet Transform , 2014 .

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

[16]  S. Devendiran,et al.  Bearing Condition Monitoring Using Tunable Q-Factor Wavelet Transform, Spectral Features and Classification Algorithm , 2018 .

[17]  Wu Deng,et al.  Semi-Supervised Broad Learning System Based on Manifold Regularization and Broad Network , 2020, IEEE Transactions on Circuits and Systems I: Regular Papers.

[18]  Xiwen Qin,et al.  The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest , 2017 .

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

[20]  Yu Guo,et al.  The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest , 2020 .

[21]  Haodong Liu,et al.  Performance Prediction Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy and Extreme Learning Machine , 2020, IEEE Transactions on Instrumentation and Measurement.

[22]  Yongbo Li,et al.  Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform , 2017 .