A Balanced Adversarial Domain Adaptation Method for Partial Transfer Intelligent Fault Diagnosis

Recently, domain adaptation has been widely investigated for solving domain shift problems in mechanical fault diagnosis. Generally, domain adaptation-based diagnosis methods assume that the source and target domains have identical label space. However, a more realistic scenario is that the label space of the target domain is a subset of the source domain, which may introduce two problems: mismatching caused by the occurrence of outlier classes and misalignment caused by overweighting of the uncertain samples near the classification boundary. To address the above problems, a balanced adversarial domain adaptation network (BADAN) is proposed for intelligent fault diagnosis tasks under partial transfer scenarios. A balanced strategy is introduced to augment classes in the target domain using source samples. On this basis, an adversarial domain adaptation method with class-level weight is designed to avoid negative transfer by filtering outlier classes, and promote positive transfer by mitigating the distribution discrepancy of shared classes. Moreover, to alleviate the misalignment problem, a complement objective function for ensuring alignment direction toward the support of the source classes is derived by minimizing their predicted scores of the incorrect classes rather than ground-truth classes. Extensive partial transfer diagnosis tasks constructed on two machines are used to demonstrate the robust and superior performance of BADAN.