An improved parameterless empirical wavelet transform for incipient fault identification of wheelset bearing

The empirical wavelet transform (EWT), along with its adaptable spectrum segmentation technique, finds extensive application in the incipient detection of rolling bearing faults. However, determining mode boundaries adaptively under strong noise interference remains a substantial challenge. Herein, an improved parameterless EWT based on the order statistics filter (OSF) is proposed to overcome this shortcoming. This approach replaces the Fourier spectrum with its envelope spectrum through OSF, and the local minima of the envelope spectrum are selected as the initial boundary to obtain the initial empirical modes. Furthermore, the adjacent initial empirical modes are combined using Pearson's correlation coefficient, and the final number and boundaries of empirical modes are automatically determined using the mean envelope entropy. The advantages of the proposed method are demonstrated through an accelerated degradation bearing test bench and a wheelset-bearing test bench, as well as by comparing it with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and Autogram.