Source-Free Progressive Domain Adaptation Network for Universal Cross-Domain Fault Diagnosis of Industrial Equipment

Recently, transfer learning (TL)-based intelligent fault diagnosis (IFD) methods have been extensively adopted in the realm of industrial equipment. A fundamental assumption that the source and target domains have matching fault types is effectively resolved. Unfortunately, existing methods fail to account for two limitations in real-world applications: 1) the existing methods are limited to specific domain adaptation (DA) scenarios, which makes it difficult to achieve satisfactory results and 2) the existing methods do not consider data privacy protection because they require both source and target samples during the training stage. To address these challenges, a novel source-free progressive DA network (SPDAN) is proposed to simultaneously handle multiple DA scenarios without accessing source samples. First, a neighbor searching-based trustworthy pairs construction is utilized to provide the high-confident nearest fault samples. Second, an instance alignment-based domain shift reduction is used to eliminate the data distribution discrepancy of different domains. Finally, an information entropy-based novel fault detection is employed to identify unknown fault samples. Experiments on two bearing datasets validate the proposed SPDAN. The experiments confirm that the proposed SPDAN can successfully operate in multiple DA scenarios without relying on source samples, making it a highly promising approach for diagnosing faults in industrial equipment.