Deep learning-based cross-sensor domain adaptation for fault diagnosis of electro-mechanical actuators

Recently, the development of intelligent data-driven machinery fault diagnosis methods have received significant attention. In most studies, the training and testing data are assumed to be collected from the same sensor. However, in real practice, due to the mounting limitation and sensor malfunctioning, it cannot be generally guaranteed to obtain the data from the same sensor location at all times. The testing and training data can be possibly from different sensor locations. Consequently, different data distributions exist, which remarkably deteriorates the data-driven model performance in different scenarios. In order to address this issue, this paper proposes a deep learning-based cross-sensor domain adaptation approach for machinery fault diagnosis. The maximum mean discrepancy is deployed as a distance metric to realize marginal domain fusion. The unlabeled parallel data is further exploited to achieve conditional domain alignment with respect to different machine health conditions. An electro-mechanical actuator dataset is used as a case study for the validation of the proposed method. Different tasks are designed to simulate different cross-sensor domain adaptation problems in fault diagnosis. The experimental results suggest the proposed method achieves higher than 95 % testing accuracies in most tasks, and it offers a promising approach for cross-sensor fault diagnosis problems. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.