Targeted transfer learning through distribution barycenter medium for intelligent fault diagnosis of machines with data decentralization

Deep transfer learning-based fault diagnosis of machines is achieved based on the assumption that the source and target domain data could be centralized to assess the distribution discrepancy. In engineering scenarios, however, data centralization is difficult to true due to data privacy and the huge cost of large-volume data transmission and analysis. To achieve transfer fault diagnosis with respect to data decentralization, federated learning comes to reform the transfer fault diagnosis methods, where the intermedia distribution could serve as a medium to indirectly evaluate the cross-domain discrepancy instead of raw data centralization. Existing works have two weaknesses: (1) the conditional distribution discrepancy is mostly ignored by federated adaptation, and (2) there is no effective approach to generate the appreciated distribution medium. For these weaknesses, we propose a federated semi-supervised transfer fault diagnosis method called targeted transfer learning through distribution barycenter medium (TTL-DBM). The TTL-DBM contains a server and two clients respectively in the source and target domain sides. In the server, the auto-encoders are stacked to aggregate the key parameters of data distributions from both domain sides, and further generate the distribution barycenter as intermedia for federated adaptation. In two clients, the adaptation trajectory towards the distribution medium is designed according to the associated labels among the fully-labeled source domain and the one-shot labeled target domain. Through collaboratively training with the server and the clients, the joint distribution discrepancy across domains is reduced in a federated setting. The TTL-DBM is demonstrated in two cases including transfer diagnosis across different machine-used bearings as well as transfer diagnosis of industrial robots. The results show that TTL-DBM could obtain similar cross-domain features through the adaptation via distribution medium, and achieve higher diagnosis accuracy than other federated adaptation methods in the presence of data decentralization.