Study on a Motor Bearing Fault Diagnosis Method Using Improved EWT Based on Scale Space Threshold Method

Abstract

Empirical Wavelet Transform (EWT) is a novel non-stationary signal analysis method that can effectively identify different mode components in signals. However, due to the lack of processing noise and unstable signals caused by the Fourier spectrum adaptive segmentation problem, an improved EWT (FCMEWT) method based on the scale space threshold method and fuzzy C-means is proposed to decompose the vibration signal into an empirical mode with physical meaning. The FCMEWT method firstly scales the spectrum of the original vibration signal, and then uses the fuzzy C-means method to classify the spectrum in order to obtain the spectrum division interval. The vibration signal is decomposed into a set of intrinsic mode functions (IMFs) components, which are performed Hilbert transform for extracting the frequency of each component through the power spectrum. Finally, Pearson correlation coefficient between each IMF component and the original signal is calculated to obtain the correlation coefficient threshold in order to determine the final IMF component. In order to verify the effectiveness of FCMEWT method, the vibration signal motor bearing is selected in this paper. The FCMEWT method is compared with the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods. The results show that the FCMEWT method can effectively solve the problem of Fourier spectrum segmentation in the EWT method, takes on better adaptive segmentation characteristics, and can effectively extract fault feature frequency of motor bearing. The fault diagnosis method can not only effectively extract motor bearing fault characteristics, but also has better diagnosis result than EMD and EEMD methods.