Few-Shot Class-Incremental Learning for System-Level Fault Diagnosis of Wind Turbine

As a complex industrial system, wind turbine (WT) will inevitably experience new faults during long-term operation. Incremental fault diagnosis can continuously accumulate new fault knowledge from data streams, thereby expanding the model's diagnostic capabilities and overcoming catastrophic forgetting. However, when dealing with complex multicomponent faults of WT and limited incremental fault samples, the incremental diagnostic model still suffers from the “stability-plasticity” dilemma. Therefore, a few-shot class-incremental learning (FSCIL) method called forward-back compatible representation is proposed to realize WT system-level fault diagnosis. First, a forward virtual prototype is designed to reserve sufficient embedding space for limited new classes by clustering the intraclass feature distribution in the base task. Second, a backward memory bank is constructed to avoid catastrophic forgetting during the multicomponent incremental process by refining representative old diagnostic knowledge. In the WT system-level fault diagnosis experiment, the superiority and efficiency of the proposed method in the FSCIL scenario are compared with mainstream incremental learning methods and validation through the ablation experiment.