Fault diagnosis method of motor bearing based on IFGO-VMD-SVM

Abstract

To solve the problems of difficult feature extraction using the VMD method and low fault diagnosis accuracy in the process of bearing fault diagnosis, this paper proposes the IFGO-VMD-SVM (Improved fungal growth optimization algorithm - variational mode decomposition - support vector machine) intelligent diagnosis method. First, the IFGO algorithm is used to optimize the parameters of Variational Mode Decomposition (VMD) to achieve fault feature extraction. Then, it is combined with SVM for fault diagnosis. Compared with the VMD-SVM models based on different optimization algorithms, experiments show that the proposed model achieves the highest diagnostic accuracy, verifying its effectiveness and superiority.