Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

Fault diagnosis of rolling bearing has been research focus to improve the productivity and guarantee the operation security. In general, traditional approaches need prior knowledge of possible features and a mass of labeled data. Due to the complexity of working conditions, it costs a lot of time to label the monitoring data. In this paper, Categorical Adversarial Autoencoder (CatAAE) is proposed for unsupervised fault diagnosis of rolling bearings. The model trains an autoencoder through an adversarial training process and imposes a prior distribution on the latent coding space. Then a classifier tries to cluster the input examples by balancing mutual information between examples and their predicted categorical class distribution. The latent coding space and training process are presented to investigate the advantage of proposed model. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method. The experimental results indicate that the proposed method achieved satisfactory performance and high clustering indicators with strong robustness when environmental noise and motor load changed.