A study on rolling bearing fault diagnosis using RIME-VMD

To address the challenges of feature extraction in Variational Mode Decomposition (VMD) for rolling bearing fault diagnosis, this paper proposes a feature extraction method optimized by the RIME algorithm, called RIME-VMD. First, under various rolling bearing fault conditions, the RIME algorithm is employed to determine the optimal combination of decomposition components and penalty factors in VMD. Next, the kurtosis values of each decomposed Intrinsic Mode Function (IMF) are calculated, and the component with the most prominent fault features is selected for noise reduction through reconstruction. Finally, the sample entropy of the reconstructed signal is calculated as a fault feature and input into a Support Vector Machine (SVM) for rapid identification and diagnosis of various rolling bearing fault types. Simulation results indicate that, compared to the Whale Optimization Algorithm optimized VMD (WOA-VMD), the RIME algorithm optimized VMD (RIME-VMD) achieves shorter search times and higher search efficiency. It facilitates faster identification of decomposition parameters under various fault conditions, enhancing the robustness of fault signal detection and enabling rapid, efficient identification of rolling bearing faults. The findings of this study offer guidance and reference for future research on rolling bearing fault diagnosis.