Federated transfer learning for machinery fault diagnosis: A comprehensive review of technique and application

As a crucial role in the prognostic and health management of mechanical equipment, fault diagnosis encounters serious challenges, such as the scarcity of fault samples, the high cost of sample labeling, the distribution discrepancy, and the data island between multiple users. Federated transfer learning (FTL)-based diagnosis methods provide a feasible and effective solution for such challenges, aiming to transfer and generalize models across domains by leveraging data from diverse sources under the preserving framework of data privacy. However, no dedicated research review comprehensively summarizes FTL-based fault diagnosis methods because its application in fault diagnosis is still in the preliminary exploration stage. Therefore, this paper develops a systematic literature review for the technique and application of FTL-based machinery fault diagnosis. This review introduces the related definition and diagnosis procedure of FTL. Then, detailed discussion and analysis are conducted on the three core elements of FTL, including privacy-preserving paradigm, federated communication framework, and distribution alignment mechanism; next, based on whether the data from the target client can be accessed during the training phase, relevant FTL-based diagnosis approaches are comprehensively discussed. Ultimately, this review is expected to help researchers better understand this intelligent diagnostic technology based on FTL, inspiring them to contribute.