WOA-VMD and SVM Rolling Bearing Failure Diagnosis

To address the issue of low accuracy in extracting fault information during rolling bearing fault diagnosis, this study proposes a method using a combination of Whale Optimization Algorithm-Variational Mode Decomposition (WOA-VMD) and Support Vector Machine (SVM). The proposed method is aimed at improving the accuracy of fault information extraction in rolling bearings. Firstly, the whale optimization algorithm is used to optimize the parameters of variational mode decomposition. Then, the optimized VMD is used to decompose the bearing signal, calculate the steepness and energy entropy of the decomposed modal component, combine the steepness and energy entropy, and construct the feature matrix. Finally, the support vector machine model is used to identify and diagnose bearing faults. Compared with the whale optimization algorithm-empirical mode decomposition model (WOA-EMD), adaptive variational modal decomposition model (AVMD), gray wolf algorithm-variational modal decomposition model (GWO-VMD), the recognition accuracy of the four methods is 83.3333%, 89.7436%,93.5897%and100%, respectively, which is obviously the accuracy of the proposed method is higher.