Sparse representation of parametric dictionary based on fault impact matching for wheelset bearing fault diagnosis

Wheelset bearing is one of the crucial rotating elements in the train bogie. Detection of wheelset bearing defect comes with many challenges due to complex wheel/rail excitation and the horrible working condition. The parametric dictionary sparse representation provides a practical path to detect the weak fault of wheelset bearing. However, the parametric dictionary obtained by the classical correlation filtering algorithm (CFA) is hard to match the analyzed signal's underlying fault impact characteristic. A novel parametric dictionary design algorithm named fault impact matching algorithm (FIMA) combining Orthogonal matching pursuit (OMP) is proposed to address the problem in this paper. The core of the FIMA mainly comprises two stages: partial segmentation and global analysis. Two indexes, correlation function (CF) and kurtosis, are used to comprehensively evaluate the partial and global matching degree between the Laplace wavelet and the signal. The proposed method's effectiveness is verified by the fault simulation analysis and the practical wheelset bearing fault signals (outer and inner race fault). Some comparison studies demonstrate that the proposed method outperforms the CFA-OMP, the K-SVD-OMP and some time-frequency decomposition methods, such as EWT and VMD, in detecting the bearing weak defects. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a novel parametric dictionary design algorithm, FIMA, combined with the OMP algorithm to detect the weak faults of wheelset bearings. Through partial segmentation and global analysis, the CF and kurtosis indexes are used to evaluate the matching degree between the signal and Laplace wavelet in a comprehensive way.