A Hybrid Method for Fault Diagnosis of Rolling Bearings

Abstract Traditional diagnostic methods often have insufficient accuracy and noise reduction, which leads to diagnostic errors. To 3 address these issues, this paper proposes an advanced fault diagnosis model that combines the variational mode decomposition 4 (VMD) improved by a Variable-Objective Search Whale Optimization Algorithm (VSWOA) with a Pelican Optimization 5 (PO)-boosted Kernel Extreme Learning Machine (KELM) algorithm. The application of the method is shown here in the fault 6 diagnosis of rolling bearings. The proposed VSWOA enhances the performance of VMD by incorporating a Sobol sequence, 7 nonlinear time-varying factors, a multi-objective initial search strategy, and an elite Cauchy chaos mutation strategy, significantly 8 improving noise reduction in vibration signals. Fault information is precisely extracted using waveform factors, sample entropy, 9 and advanced composite multiscale fuzzy entropy, which enables effective feature screening and dimensionality reduction. The