Intelligent diagnosis of rolling bearings with imbalanced data based on the ConVAE-CNN model

Abstract

Data-driven intelligent diagnosis method has been widely used in mechanical equipment fault identification. However, the data set imbalance is still one of the critical problems affecting the effect of intelligent diagnosis in the practical work of rotating machinery. To solve the problem of fault identification of rolling bearings with imbalanced data sets, a rolling bearing intelligent diagnosis method based on the ConVAE-CNN model was proposed in this paper. In this method, a convolutional variational autoencoder network (ConVAE) model is constructed. In the coding stage, the convolutional layer is used to extract the features of minority samples. In the decoding stage, full connection layers are used to expand the dataset of minority samples. Bearing fault features were extracted by convolution and pooling operations of the convolutional neural network model, and the performance of the proposed method was tested using rolling bearing experimental data. The results show that the proposed method effectively solves the bearing fault classification problem with imbalanced data sets. Compared with the other diagnosis methods, the performance of the proposed method is better, and it has a broad application prospect in the diagnosis of imbalanced data.