Rolling bearing fault diagnosis method based on SSA-IWT-EMD

Abstract

Aiming at the problem of insufficient wavelet threshold noise reduction and unclear extraction of characteristic frequency of EMD decomposition, a rolling bearing fault diagnosis method based on SSA-IWT-EMD is proposed. Two adjustment factors are introduced to propose an improved threshold function (IWT), which overcomes the shortcomings of traditional soft and hard thresholds, and the sparrow search optimization algorithm (SSA) is used to globally optimize its parameters to achieve rolling bearing signal noise reduction. A comprehensive index P is proposed to select and reconstruct the components generated by empirical mode decomposition (EMD) to highlight the fault characteristic information of the signal. Envelope spectrum analysis is used to realize bearing fault diagnosis. Simulation and measurement results verify the effectiveness of the proposed method; at the same time, compared with the method of selecting components with a single index and the literature method, it is shown that the comprehensive index P and the proposed method have stronger noise reduction and feature extraction capabilities, and the envelope spectrum amplitude and frequency doubling components are more obvious, which can better realize the fault diagnosis of rolling bearings.