Knowledge Distillation From Electromagnetic Simulation for Asynchronous Motor Fault Diagnosis With Small Labeled Samples

Accurate fault diagnosis of asynchronous motors is critical in industry, and intelligent diagnosis methods have demonstrated notable performance. However, the scarcity of labeled data in real-world scenarios constrains model performance, while efficiently extracting fault-related features from three-phase stator current signals remains a persistent challenge. In this paper, a new method of knowledge distillation from high-fidelity electromagnetic simulations to actual measurement data is proposed. Firstly, Empirical Wavelet Transform Recurrence Plot (EWT-RP) is proposed to adaptively decompose fault-induced components from current signals and convert them into two-dimensional (2D) colored images, emphasizing variances across both time and phases. A self-attention distillation strategy incorporating feature and attention distributions is designed to enable the model to inherit the capability of attending to fault-related representations from simulations. Additionally, a novel Class-aware Sinkhorn Distance (CASD) is proposed to achieve intra-class knowledge transport. Comparative and ablation experiments conducted on an asynchronous motor under two operating conditions validate the effectiveness of the proposed method.