An improved empirical wavelet transform method for rolling bearing fault diagnosis

Abstract

Empirical wavelet transform (EWT) based on the scale space method has been widely used in rolling bearing fault diagnosis. However, using the scale space method to divide the frequency band, the redundant components can easily be separated, causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively. This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set. The frequency domain feature parameter set includes two characteristic parameters: mean and variance. After adaptively dividing the frequency band by the scale space method, the mean and variance of each band are calculated. Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis. An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode. The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes. The empirical mode with the largest margin factor is selected to envelope spectrum analysis. Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods.