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

Abstract

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.