A Few-Shot Domain Class Incremental Motor Fault Diagnosis Method With Dynamic Anchor Prototyping

Abstract

Domain incremental class learning has been studied in the fault diagnosis of motors and other mechanical equipment. However, in domain-incremental few-shot scenarios, existing incremental methods, troubled by feature confusion and overfitting, lead to low fault-classification accuracy of mechanical systems under dynamic conditions and fail to meet the need for real-time monitoring of operating-condition switches. Therefore, this article proposes a few-shot domain class incremental motor fault diagnosis method with dynamic anchor prototyping (DAP). In the initial training stage, the anchor loss constrains intra-class feature compactness. Meanwhile, a domain-decoupled and class-separable feature space is built to suppress catastrophic forgetting from old-new knowledge confusion in the incremental stage. In the incremental stage, a dynamic class prototype classifier is designed to adaptively align few-shot features based on the initial domain pre-training parameters to achieve efficient extraction of cross-domain key discriminative features without back-propagation iterations, which significantly reduces the training time. The motor fault diagnosis experimental results demonstrate that the proposed method achieves higher accuracy with the lower forgetting rate in comparison to traditional incremental learning methods.