Fault Diagnosis Method Based on Distributed Online Collaborative Distillation

Distributed networks and systems are becoming more and more widespread and practical in applications. The distributed network has a large scale and a large number of edge terminals, which generate massive data during operation. As far as fault diagnosis is concerned, traditional techniques face many challenges. The paper proposes a Fault Diagnosis Method Based on Distributed Online Collaborative Distillation (DOCD) for distributed and decentralized scenes. To adapt to the large-scale distributed network and the unstable communication quality scenario, the method distributes the training tasks among the terminal nodes to eliminate the strong dependence of each terminal on the cloud server. Each terminal processes the local data separately and trains the fault diagnosis model locally. Through online optimized collaborative distillation, each terminal model exchanges information and knowledge to improve the diagnostic capability of global data. Finally, the effectiveness of the proposed method is proved by experiments.