Repetitive Transients Extraction Algorithm for Detecting Bearing Faults
This addresses bearing fault diagnosis for industrial maintenance, but it appears incremental as it builds on existing sparsity-based methods.
The paper tackled noise reduction and feature extraction in vibration signals for bearing fault diagnosis by proposing an optimization-based approach with convex and non-convex regularization to extract repetitive transients, demonstrating effectiveness in detecting outer and inner race defects in locomotive bearings.
This paper addresses the problem of noise reduction with simultaneous components extrac- tion in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.