Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Abstract

Due to the coupling of multiple fault feature information and contamination of heavy background noise, it is a challenging task to accurately identify rolling bearing compound faults (RBCFs). A method for isolating and identifying the RBCF is proposed by integrating adaptive periodized singular spectrum analysis (APSSA) with Rényi entropy (RE). The adaptive selection of the embedding dimension of the Hankel matrix in APSSA without setting parameters empirically is proposed, and a selection criterion for singular values is established to preprocess the vibration signals of the rolling bearing and enhance the periodic component of the fault. An RE-based threshold value is introduced to further isolate and decouple the impulse segments of the vibration signal in the time domain. By considering the inner raceway fault, outer raceway fault, ball fault, and skidding, a comprehensive simulation model of the compound fault is constructed by the response mechanism of different excited resources. Simulated and experimental data are applied to validate the effectiveness and practicability of the proposed method. The results demonstrate that the RBCF can be identified correctly by the proposed method under strong background noise.