A Novel Fault Diagnosis Method Based on Improved Empirical Wavelet Transform and Maximum Correlated Kurtosis Deconvolution for Rolling Element Bearing

doi:10.3901/JME.2019.23.136

Abstract

Abstract: In order to solve the problem of Empirical wavelet transform method for the rolling element bearing fault diagnosis in strong noise condition, that is mainly the inappropriate segmentation of the signal spectrum, the combination of the Improved Empirical wavelet transform and Maximum correlated kurtosis deconvolution method is proposed. Firstly, an original signal is de-noised with MCKD algorithm, and the max kurtosis of its envelope spectrum is taken as an objective to detect its periodic impact components. Then, the envelope of the signal Fourier spectrum is segmented based on the peaks, and the most meaningful component can be found from the signal components. At last, fault features can be diagnosed by analyzing obvious frequency components in squared envelope spectrum. The new method can de-noise the signal and enhance the periodic impact components feature. It is shown that the lesser powerful fault induced by Single accidental impact and nonimpact components is restrained in de-noised signal. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals. It's shown that fault character extracted by the proposed method is more clearly and believable than the fast kurtogram algorithm and wavelet envelope analysis.

Key words: empirical wavelet transform, fast kurtogram algorithm, maximum correlated kurtosis deconvolution, wavelet envelope analysis, rolling bearing

CLC Number:

TH165

LI Zheng, ZHANG Wei, MING Anbo, LI Zheng, CHU Fulei. A Novel Fault Diagnosis Method Based on Improved Empirical Wavelet Transform and Maximum Correlated Kurtosis Deconvolution for Rolling Element Bearing[J]. Journal of Mechanical Engineering, 2019, 55(23): 136-146.

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