Bearing fault diagnosis method based on enhanced VMD and adaptive-optimized SDAE.

ABSTRACT: Motor rolling bearing is a fundamental component of industrial production, and its vibration signal extraction and fault diagnosis are challenging because of the effect of operating characteristics and external noise. This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic m

SUBMITTER: Ren X 

PROVIDER: S-EPMC12677484 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature