Impulsive wavelet based probability sparse coding model for bearing fault diagnosis

It has become a challenge to accurately extract weak bearing fault features from early fault stage. To solve this problem, a novel fault features extraction method called improved Kurtogram and Hyper-Laplacian Sparse Coding (KurHLSC) based on probability sparse coding is proposed in this paper. The originality of the present article lies in the construction of a sparse coding model considering probability and wavelet dictionary, which can effectively decompose sparse fault features even in strong noise. Moreover, in order to eliminate the interference of random pulse on sparse coding model, the improved kurtogram method successfully achieved filtering. The effectiveness of KurHLSC in rolling bearing fault diagnosis is verified by simulation studies and run to-failure experiments, and the comparison studies showed that KurHLSC has better estimation results than other existing methods.