Enhanced generalized nonlinear sparse spectrum based on dual-tree complex wavelet packet transform for bearing fault diagnosis

Abstract

The generalized nonlinear sparse spectrum (GNSS), as an improved fast kurtogram (FK) method, effectively suppresses the interference of abnormal signals through nonlinear preprocessing and sparse enhancement. However, the GNSS method inherits the shortcoming of the traditional FK method, using finite impulse response filters to process nonstationary signals, which limits the accuracy of fault extraction. Therefore, more precise filters should be developed to further improve the performance of fault features. Inspired by this, this paper introduces the dual-tree complex wavelet packet transform (DTCWPT) into the sparse spectrum, and proposes an enhanced generalized nonlinear sparse spectrum (EGNSS) for bearing fault diagnosis. Firstly, nonlinear preprocessing is performed on the input signal to weaken the interference of abnormal impacts. Secondly, the generalized pq-mean value of each subband obtained by DTCWPT is calculated. Finally, the sparse spectrum is constructed and the signal reconstruction is performed on the frequency band where the maximum generalized pq-mean value is located for the envelope analysis. Simulation signals and experimental bearing fault signals have been applied to demonstrate the superiority of the proposed EGNSS in improving fault performance and accuracy.