Intelligent fault diagnosis methodology under varying operating conditions using multi-layer domain adversarial learning strategy

In the past decades, data-driven methods for the machinery fault diagnosis problem have been developed successfully, especially for the tasks where the training data and the testing data are from the same distribution. In the real industrial scenarios, because of the diversity of the practical factors, the training data and the testing data are generally from different distributions which leads to data distribution discrepancy. Most existing well-established methods basically cannot well address this problem. In this paper, a new multi-layer domain adversarial learning strategy is proposed for transfer learning. Adversarial training in multiple layers is implemented to achieve domain fusion under varying operating conditions. The experiments on the real-world rolling element bearing dataset are carried out for validation, and promising testing accuracies is achieved in different tasks, which are higher than the other popular methods. The experimental results verify the validity of the proposed method on the problem of the cross-domain fault diagnosis, and the applicability in the real industrial scenarios.