ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN - CHEN DaiJun; CHEN LiLi; DONG ShaoJiang (Jixie qiangdu)
Aiming at problems that traditional fault diagnosis methods need to extract features manually and the recognition rate is low, a VMD-CWT-CNN model based on variational modal decomposition (VMD) and continuous wavelet transform (CWT) combined with convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, the bearing vibration signal is decomposed into multiple modal components with different center frequencies by VMD. Secondly, the modal components are calculated by CWT and transformed into two-dimensional time-frequency diagram. Finally, the time-frequency diagram is input into the EfficientNet convolution neural network after structure cutting, the features are automatically extracted, and the fault diagnosis of rolling bearing is completed. Using the method proposed, the average accuracy of multiple experiments on 10 types of bearing fault data from Case Western Reserve University is 99. 86%, which can effectively complete the feature extraction of rolling bearing signal and the accurate diagnosis of damage degree.