Rolling bearing fault diagnosis method based on WOA-VMD and CNN-SVM

Aiming at the low accuracy of fault identification caused by insufficient fault feature extraction in vibration signals of rolling bearings, a fault diagnosis method based on whale algorithm to optimize variational modal decomposition parameters (WOA-VMD) for feature extraction and convolution neural network coupled with support vector machine (CNN-SVM) is proposed. Firstly, the parameters of VMD are optimized by WOA algorithm, and then some intrinsic modal components (IMF) are obtained by decomposing the fault signal by the VMD method. Then the IMF components are screened by correlation coefficient method, and the sample envelope entropy is further extracted as the feature vector. Finally, CNN-SVM classifier is used as a fault identification method to identify the faults of rolling bearings. The experimental results show that the WOAVMD feature extraction method can accurately extract the fault information of rolling bearing vibration signals, and CNN-SVM classifier can effectively identify the fault features in bearing vibration signals. Compared with SVM and PSO-SVM classification methods, the proposed method can improve the fault recognition rate, and the accuracy rate can be improved to 99.6%.