A novel method for diagnosing bearing transfer faults based on a maximum mean discrepancies guided domain-adversarial mechanism
Meixia Jia, Jinrui Wang, Zongzhen Zhang, Baokun Han, Zhaoting Shi, Lei Guo, Weitao Zhao
Abstract
Abstract Transfer learning has been successfully applied in fault diagnosis to solve the difficulty in constructing network models due to the lack of labeled data in practical engineering. The current transfer learning models mainly use the adaptive method to obtain the similarity between source and target domains, but the obtained similarity is incomplete. Inspired by the domain-adversarial mechanism, a novel method called ‘distance guided domain-adversarial network’ (DGDAN) is proposed in this study. DGDAN includes two modules: domain-adversarial network and maximum mean discrepancies (MMD) guided domain adaptation. In this method, a stacked autoencoder (SAE) is used as the feature extractor of the domain-adversarial network to learn domain invariant features, and MMD is used to measure the non-parametric distance of different metric spaces to improve domain alignment. Reduction of the distance of the bottleneck layer of the feature extractor is employed to improve the feature extraction capability of the network. Experimental results show that the classification accuracy rate of DGDAN is more than 98%, and DGDAN has superior robustness and generalization ability.