A Prototype Learning Framework Based on Continual Learning for Motor Incremental Fault Diagnosis Under Few-Shot Conditions

In the actual industrial scenario, motor fault classes gradually increase, and the fault data need to be acquired incrementally during the service. Continual learning (CL) has been introduced to the field of fault diagnosis (FD), aiming at constructing FD model with continuous evolution capability. However, the current research has the following limitations: 1) Assigning parameters for each task independently leads to linear increase in model complexity; 2) It relies heavily on the quality and completeness of historical data, and the research on incremental FD under few-shot conditions is not sufficient. To address the above limitations, this paper proposes a prototype learning framework for motor incremental fault diagnosis (PLFIFD) under few-shot conditions. First, a prototype classifier based on distance metric is proposed to avoid uncontrollable changes due to the update of parameters in the output layer. The prototype set is dynamically expanded instead of assigning network parameters individually. Second, a multi-objective loss function is designed to jointly optimize the classification boundary and prototype space distribution to enhance intra-class compactness and inter-class separability, and to improve the generalization ability under few-shot conditions and stability in dynamic scenarios. Finally, the effectiveness of PLFIFD is verified on induction motor (IM) and permanent magnet synchronous motor (PMSM).