Gearbox Incremental Fault Diagnosis Based on the Stability–Plasticity Synergy Framework

As a key component of the rotating machinery, the gearbox inevitably develops new fault types during long-term operation. Incremental fault diagnosis can continuously assimilate the knowledge of new faults from streaming data, thereby expanding the model’s capabilities and mitigating catastrophic forgetting. However, in multi-phase incremental settings, the models are prone to the stability-plasticity dilemma. To address the above problem, we present an incremental fault diagnosis method based on a channel-plasticity and dual-memory replay (CPDMR) framework. This method enables continual diagnosis of emerging faults without retraining on the full historical dataset. First, we construct a channel-plasticity convolutional network to evaluate channel effectiveness from channel activation and weight magnitudes. Inefficient channels are then selectively reinitialized to better explore the parameter space and maintain the model’s long-term plasticity. Second, we design a dual-memory replay strategy. A long-term memory buffer preserves important historical information via representative samples. In contrast, a short-term memory buffer organized in a first-in-first-out manner optimizes learning in the current phase. The standard classification loss, a knowledge-distillation loss, and a label-smoothing loss are incorporated to achieve stable and efficient incremental fault diagnosis. Finally, the effectiveness of the proposed method is validated on the experimental gearbox data.