Repetitive Transients Extraction Algorithm for Detecting Bearing Faults

TL;DR

This paper introduces a novel convex optimization-based method for extracting repetitive transient features from vibration signals to improve bearing fault diagnosis, effectively separating noise and fault-related components.

Contribution

It proposes a convex sparsity-regularized optimization approach that simultaneously extracts multiple repetitive transient components in noisy vibration signals, enhancing fault detection accuracy.

Findings

Successfully extracts fault-related transients in synthetic signals

Effectively detects outer and inner race defects in locomotive bearings

Outperforms existing methods in noise reduction and feature extraction

Abstract

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.