A sensitive spectrum entropy-assisted Bayesian online anomaly inference method for bearing incipient degradation dynamic detection.

Incipient degradation dynamic detection is crucial for preventing serious accidents in the context of rolling bearing online automatic condition monitoring and preventive maintenance. This article presents a novel framework, cyclostationarity-sensitive spectrum fuzzy entropy-assisted Bayesian online anomaly inference (CSFE-BOAI), to address this challenge. A new health index, CSFE, is first defined by performing the fuzzy entropy measure on the extracted cyclostationarity-sensitive spectra to promote incipient-degradation sensitivity and robustness to interferences.

Multiband weights-induced periodic sparse representation for bearing incipient fault diagnosis.

Faulty impulses from incipient damaged bearings are typically submerged in harmonics, random shocks, and noise, making incipient fault diagnosis challenging. The prerequisite to this problem is the robust estimation of faulty impulses; thus, this paper proposes a multiband weights-induced periodic sparse representation (MwPSR) method. Firstly, a multiband weighted generalized minimax-concave induced sparse representation (MwGSR) approach is presented to accelerate the sparse approximation process and eliminate the interference components.

Periodicity-enhanced sparse representation for rolling bearing incipient fault detection.

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.