A Federated Learning Framework for Cloud–Edge Collaborative Fault Diagnosis of Wind Turbines

In modern Internet of Things-enhanced wind power systems, most existing data-driven fault diagnosis approaches for wind turbines (WTs) are performed under a centralized paradigm that ignores data privacy. Recently, federated learning (FL) presented a solution to enable edge WTs located at isolated sites to collaboratively learn a shared diagnosis model without accessing local privacy-sensitive data. However, the practical issues of fault label heterogeneity among edge clients and scarcity of labeled data still severely impede the generation of a satisfactory diagnosis model. To address these issues, we propose a diagnostic knowledge-based FL framework (DKFLWT) for collaborative fault diagnosis of distributed edge WTs. In our DKFLWT framework, independently learned diagnostic knowledge from each edge client, rather than model parameters in conventional FL, is uploaded to the cloud server to enrich the client-specific information visible to the server and mitigate the adverse effects on model performance caused by label heterogeneity. To enhance the overall efficiency of the framework, we develop a two-stage, single-round training mechanism, in which the cloud server serves as a universal platform that can accommodate the customized requirements of users, implying the convenient integration of semi-supervised learning to enhance the diagnosis performance in scenarios with limited labeled data. Furthermore, a spatiotemporal memory-enhanced autoencoder is designed to sufficiently exploit essential diagnostic knowledge of different fault patterns from each client. Experimental results demonstrate superior diagnosis performance of our DKFLWT framework with an improvement of more than 22.1% in accuracy and 37.2% in training efficiency against several compared methods in all seriously heterogeneous scenarios.