面向小样本场景的风机多增量故障诊断

As a complex industrial device, wind turbines will inevitably develop new faults during prolonged operation. Incremental fault diagnosis can continuously accumulate new fault knowledge from ongoing data streams, thus expanding diagnostic capabilities of the model and overcoming catastrophic forgetting. However, in scenarios with complex multi-incremental stages and small fault samples for wind turbines, the incremental diagnostic model still faces the “stability-plasticity” dilemma. Therefore, a dynamic memory strategy-driven incremental fault diagnosis method is proposed to achieve intelligent diagnosis of wind turbines in multi-increment stages under limited fault sample scenarios. First, an adaptive example repository module is constructed, autonomously refining and storing representative samples of previous categories to prevent catastrophic forgetting during the multi-incremental diagnostic process. Secondly, a weight dynamic correction algorithm is designed to help the model real-time adjust the importance between different category nodes, facilitating flexible learning of diagnostic knowledge from limited fault data. In the diagnostic experiments during the multi-incremental stages of small fault samples in the wind turbine, the superiority of the proposed method is validated by the multidimensional comparison with current mainstream incremental learning methods.