Enhancing Intelligent Cross-Domain Fault Diagnosis Performance on Rotating Machines with Noisy Health Labels

In the recent years, the intelligent data-driven fault diagnosis methods on the rotating machines have been popularly developed. Especially, deep learning algorithms have been adopted in several studies and promising results have been obtained. However, the cross-domain fault diagnostic problem still remains a challenging issue, where the training and testing data are collected from different operating conditions of the machine. To bridge the domain gap in the training and testing data and increase the model generalization ability, a domain adaptation approach is proposed in this paper within the deep learning framework. Multiple convolutional operations are employed for automatic feature extraction. The maximum mean discrepancy is used to optimize the distributions of the learned features for accurate diagnosis. To further enhance the model generalization ability, the health condition labels of the data are injected with additional noise during model training. Experiments on a bearing fault diagnosis dataset are carried out for validation. The results show that, compared with the popular transfer learning algorithms, the proposed method with noisy health labels can successfully improve the cross-domain diagnosis performance on rotating machines.