Incremental Learning of Bearing Fault Diagnosis Via Style-Based Generative Adversarial Network

Abstract

At present, transfer learning of machine fault is a relatively popular research, its main problem is the imbalance of training data caused by the lack of actual fault data. The existing incremental learning model cannot solve the entanglement problem of sample features, and the ability to obtain new samples by combining features is limited. In this paper, Style-based Generative Adversarial Networks (StyleGAN) is used to map the data features to intermediate latent space, and then generate data by recombining features. StyleGAN realizes the complete separation of signal features. Therefore, StyleGAN can be used as a tool of data incremental learning to enrich the original data, solve the problem of imbalance between training data and test data, and achieve the goal of improving the accuracy of fault classification in the later stage. In the process of training, the category label is used as the auxiliary information to help the training model. The data of training set is enhanced, and the accuracy of fault diagnosis and classification is improved, the accuracy of fault classification network model is increased from 81.4% to more than 90%, so the validity of this method is proved.