A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis

In modern industrial equipment maintenance, transfer learning is a promising tool that has been widely utilized to solve the problem of the insufficient generalization ability of diagnostic models, caused by changes in working conditions. However, owing to the single knowledge transfer source and fuzzy marginal distribution matching, the ability of traditional transfer learning methods for cross-domain fault diagnosis is not ideal. In practice, collecting multi-source data from different scenarios can provide richer generalization knowledge, and fine-grained information matching of relevant subdomains can achieve more accurate knowledge transfer, which is conducive to the improvement of the cross-domain fault diagnosis performance. To this end, a multi-source subdomain adaptation transfer learning method is proposed to transfer diagnostic knowledge from multiple sources for cross-domain fault diagnosis. This approach exploits a multi-branch network structure to match the feature spatial distributions of each source and target domain separately, where the local maximum mean discrepancy is used for fine-grained local alignment of subdomain distributions within the same category of different domains. Moreover, the weighted score of a source-specific is obtained according to its distribution distance, and multiple source classifiers are combined with the corresponding weighted scores for the joint diagnosis of the device status. Extensive experiments are conducted on three rotating machinery datasets to verify the effectiveness of the proposed model for cross-domain fault diagnosis.