Abstract
With the rise of growing privacy concerns and "data silos," federated learning is gaining traction in wind turbine fault diagnosis. Most existing methods unrealistically assume that fault classes remain static over time. However, wind turbine data are collected as dynamic streams. Existing methods necessitate the storage of all historical fault class data and require real-time model retraining using this data whenever new faults are introduced. The high demands for storage, computation, and bandwidth are impractical as new data keeps coming in. Additionally, when these methods are applied to diagnose new fault classes in dynamic data streams with limited resources and heterogeneity across wind farms, the model suffers from significant memory degradation. To address these challenges, a federated incremental collaborative fault diagnosis method for dynamic data streams across multiple wind farms is proposed. First, a new fault class detection method is presented to ensure when and where to introduce new fault classes. Second, a balance between the plasticity and stability of the fault diagnosis model at each wind farm is proposed to alleviate the fading memory problem. Third, a global model adaptive compensatory method is presented to address the fading memory issue of the aggregated model caused by heterogeneity. Finally, the proposed method was validated with data from three real-world wind farms in Hubei, Jiangsu, and Yunnan provinces, China. The results showed that it effectively mitigates fading memory issues and outperforms several state-of-the-art methods.