A Novel Open-Set Cross-Domain Fault Diagnosis Method Based on Improved PACDAN

In recent years, domain adaptation (DA) methods have been widely applied in cross-domain fault diagnosis. However, these methods are based on the assumption that the source domain and target domain share the same fault types. In practical operation, unknown fault types may occur in mechanical equipment, leading to a mismatch between the fault types of the source domain and the target domain. To address the diagnostic challenges in open-set cross-domain tasks, an improved parallel adversarial complementary DA network (PACDAN) is established. First, a parallel channel complementary attention mechanism (PCCAM) module is constructed, in which the dual-parallel branching structure and the multiscale feature interaction mechanism are designed so that the model’s ability to pay attention to key information is improved. Second, a parallel adversarial complementary (PAC) module is designed, a macroscopic classification perspective is added, and the shortcoming of a single view of fault classification is overcome. Finally, experiments are conducted on three public datasets, systematically comparing the performance of the proposed method with existing methods. Through visualization techniques and ablation experiments, the results are intuitively presented and analyzed, validating the effectiveness of the proposed method.