Memory Residual Regression Autoencoder for Bearing Fault Detection

Anomaly detection is the cornerstone for the health management of rolling element bearings. The unsupervised learning model for anomaly detection driven only by normal data has received increasing attention in recent years. In this article, an innovative deep-learning-based model, namely, memory residual regression autoencoder (MRRAE), is developed to improve the accuracy of anomaly detection in bearing condition monitoring. The memory module and autoregressive estimator are applied to calculate the probability density distribution of the latent memory residual representation. The reconstruction errors and surprisal values of the proposed model are used to detect the abnormal condition of bearing. To verify the superiority of the proposed method in anomaly detection, two sets of run-to-failure experimental data set gathered from the laboratories are studied and analyzed. The result demonstrates that the proposed MRRAE model achieves superior performance compared with several conventional and deep-learning-based anomaly detection methods. Furthermore, the proposed method pays close attention to the special structure of bearing vibration signal and provides a new way for explaining the decision-making processes of deep neural networks.