This paper addresses the challenge of extracting weak early fault signals from rolling bearings under variable speed conditions, where strong background noise often obscures diagnostic features. We propose a novel fault diagnosis method that integrates variational mode decomposition (VMD) and maximum second-order cyclo-stationarity blind deconvolution (CYCBD). The proposed approach begins by converting non-stationary vibration signals into angular-domain stationary signals using computed order tracking (COT). Subsequently, the parameters of the VMD algorithm are optimized via the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) to select the optimal modal components. A key contribution is the introduction of a composite index (CI), combining harmonic significance and the envelope spectrum crest factor, which serves as the fitness function for the SCSSA to optimize the critical parameters of CYCBD for enhanced feature enhancement. Finally, fault characteristics are extracted by analyzing the deconvolved signal with an order envelope spectrum. Both simulation and experimental results demonstrate the superior capability of the proposed VMD-CYCBD method in effectively identifying weak fault features submerged in strong noise under variable speed conditions.
Wang et al. (Fri,) studied this question.