A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis

Abstract

Multi-source domain adaptation, an effective solution for rotating machinery fault diagnosis, has achieved great success. However, previous multi-source domain adaptation based methods typically achieve domain consistency by aligning the global distribution of the source and target domains, regardless of private domain information and sufficient feature transferability, thereby limiting the diagnosis performance in cross-domain diagnostic scenarios. To address these issues, we propose a novel fine-grained feature decoupling based multi-source domain adaptation network (FD-MDAN) for fault diagnosis. Leveraging fine-grained feature decoupling, our FD-MDAN weakens specific features with private domain information and reinforces invariant features with diverse transferability at both of domain and category levels. We further develop a diversity regular term to minimize the feature redundancy, enhancing the transferability. In addition, to ensure that these decoupled features retain representative information of the original data, a reconstruction module is designed to make the reconstructed data and the original data consistent. Extensive experiments are conducted on three rotating machinery datasets. Benefiting from these components, our proposed FD-MDAN demonstrates state-of-the-art performance on various transfer diagnosis tasks.