Early fault diagnosis of bearing using empirical wavelet transform with energy entropy

A novel bearing fault diagnosis method is proposed in this paper, which is based on Empirical Wavelet Transform (EWT) with energy entropy. In this method, first the vibration signal of rolling bearing is decomposed by the EWT, and several AM-FM components are obtained. Then calculated the energy entropy of each IMFs respectively and the sensitive IMF is selected according to the value of energy entropy. Finally, the Hilbert transform to the sensitive IMF and using the envelope spectrum to detect bearing fault characteristic frequency. The experiment results show that the proposed method is accurate and can provide a good performance in the outer race, inner race and rolling element faults detection.

[1]  Zhong Liang Lv,et al.  The Application of EEMD to Fault Diagnosis of Rolling Bearing , 2013 .

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

[3]  Binqiang Chen,et al.  A pseudo wavelet system-based vibration signature extracting method for rotating machinery fault detection , 2013 .

[4]  B. Merainani,et al.  Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform , 2017 .

[5]  Belkacem Ould-Bouamama,et al.  Rolling bearing fault diagnosis based empirical wavelet transform using vibration signal , 2016, 2016 8th International Conference on Modelling, Identification and Control (ICMIC).

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

[7]  Jing Yuan,et al.  Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review , 2016 .

[8]  Adem Çiçek,et al.  Vibration Analysis of Rolling Element Bearings Defects , 2014 .

[9]  Fei Hu,et al.  Rolling Bearing Fault Degree Recognition Based on Ensemble Empirical Mode Decomposition and Support Vector Regression , 2013 .

[10]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

[11]  Zhao Ying-kai.  Fault severity assessment for rolling element bearings based on EMD and Lempel-Ziv index , 2010 .

[12]  Shuren Qin,et al.  Research on the unified mathematical model for FT, STFT and WT and its applications , 2004 .

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

[14]  William D. Mark,et al.  A simple frequency-domain algorithm for early detection of damaged gear teeth , 2010 .

[15]  Antoine Tahan,et al.  A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis , 2016 .

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

[17]  Haizhao Yang,et al.  Synchrosqueezed wave packet transforms and diffeomorphism based spectral analysis for 1D general mode decompositions , 2013, 1311.4655.

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

[19]  Enrico Zio,et al.  Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method , 2014, Digit. Signal Process..