Periodicity-enhanced sparse representation for rolling bearing incipient fault detection

Abstract

Incipient fault detection of rolling bearings is a challenging task since the weak fault features are disturbed by heavy background noise. This paper develops a periodicity-enhanced sparse representation method to address this issue. Firstly, periodicity-enhanced basis pursuit denoising (PBPD) is proposed by the theoretical derivation. Fault proportion is defined to quantify the single fault severity of sparse signals, then a periodicity-decision criterion for determining the optimal potential fault period is designed to periodically filter the last sparse signal. Secondly, the suitable linear transformation for PBPD is investigated in comparison and maximal overlapping discrete wavelet packet transform is adopted eventually. Thirdly, adaptive selection strategies are developed for the key parameters of PBPD. Simulations and experimental verifications demonstrate PBPD’s excellent performance in rolling bearing incipient fault detection.