A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis

Abstract Amplitude demodulation is a key means of diagnosing bearing faults. The quality of demodulation determines the effectiveness of spectrum analysis in detecting defects. However, the quality of the demodulated signal depends on the frequency band selected for demodulation. In this paper, a new method combining the empirical wavelet transform (EWT) and operational modal analysis (OMA) is proposed. EWT acts like a filter bank in which the support boundaries of the filter are defined using OMA. The proposed method (OMA–EWT) decomposes the signal into multiple components and kurtosis values are used to select automatically the components for performing the envelope spectrum in order to extract the frequency related to the defect. The method is validated on two test benches and a comparative study is conducted with the kurtogram. The results show that the combined OMA–EWT method can improve EWT for decomposing the signal into multiple components. Using OMA–EWT, a selection of all the components excited by the defect gives more accurate diagnostic results.

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

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

[3]  Antoine Tahan,et al.  Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO , 2014 .

[4]  Fanrang Kong,et al.  Bearing fault diagnosis based on an improved morphological filter , 2016 .

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

[6]  Ying Zhang,et al.  Classification of fault location and performance degradation of a roller bearing , 2013 .

[7]  Zhipeng Feng,et al.  Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation , 2012 .

[8]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[9]  Jinglong Chen,et al.  Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment , 2016 .

[10]  Zhengjia He,et al.  Wheel-bearing fault diagnosis of trains using empirical wavelet transform , 2016 .

[11]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

[12]  T. Y. Wu,et al.  Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach , 2009 .

[13]  C. Y. Yang,et al.  Diagnostics of gear deterioration using EEMD approach and PCA process , 2015 .

[14]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

[15]  Xia Wang,et al.  Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension , 2013 .

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

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

[18]  Marc Thomas,et al.  Operational modal analysis by updating autoregressive model , 2011 .

[19]  François Lafleur,et al.  Towards an automatic spectral and modal identification from operational modal analysis , 2013 .

[20]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[21]  Hu Xiao,et al.  Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance , 2015 .

[22]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[23]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

[24]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

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

[26]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[27]  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.

[28]  I. R. Praveen Krishna,et al.  Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis , 2014 .

[29]  Trapti Jain,et al.  Estimation of Single-Phase and Three-Phase Power-Quality Indices Using Empirical Wavelet Transform , 2015, IEEE Transactions on Power Delivery.

[30]  Jinshan Lin,et al.  Improved Ensemble Empirical Mode Decomposition and its Applications to Gearbox Fault Signal Processing , 2015 .