An Extensible Framework Based on Graph-Guided Sample Replay for Motor Incremental Fault Diagnosis

In the actual industrial scenarios, where the fault classes of motor appear progressively, fault diagnosis (FD) systems can only acquire fault data incrementally during service. Deep learning-based methods tend to obtain a fixed model, which is difficult to adapt to the dynamic scenarios with increasing fault classes. To address the above problems, an extensible framework based on graph-guided sample replay for motor incremental fault diagnosis (GSRIFD) is proposed in this paper. First, a dynamic and extensible model updating mechanism is proposed, which achieves a balance between the old and new knowledge and avoids complex network branches by sharing general feature representation layers and adjusting high-level feature extraction layers. Second, a representative exemplars selection mechanism is proposed to select the most representative exemplars by constructing a relation graph based on similarity. In addition, knowledge distillation (KD) is introduced to assist training to extract the knowledge of the old model. The framework can establish an adaptive diagnostic model with stable performance in dynamic industrial scenarios. Finally, experiments are conducted on induction motor (IM) and permanent magnet synchronous motor (PMSM), and the experimental results validate the effectiveness of GSRIFD.