Deep convolution variational autoencoder network based transfer learning strategy for fault diagnosis

Abstract

The successful application of traditional machine learning to mechanical fault diagnosis relies on two conditions: the same probability distribution of training data and testing data, and the data containing fault information has labels. However, it is difficult to obtain massive labeled data, and the change of mechanical operation conditions also results in inconsistent distribution of source domain data and target domain data, which makes the labeled data for training model possibly fail in classifying unlabeled data acquired under other conditions. Aiming at solving the above problems, a deep convolution variational autoencoder network is introduced, the pseudo-label information of small samples in target domain is predicted by using label propagation and data fusion methods, combining with the domain adaptability advantages of transfer learning theory, a novel diagnosis method based on transfer learning with deep convolution variational autoencoder (TL-DCVAEN) is presented. In addition, the spectrum data is used as the input of the model to reduce the dependence on artificial feature design and engineering experience. The experimental results indicate that the proposed diagnosis method is suitable for rolling bearing fault diagnosis under variable working conditions, and has better diagnostic performance and generalization.