Domain Adaptive Induction Motor Diagnosis Using Residual Maximum Kernel Discrepancy Adaptation Network

Deep learning-based fault diagnosis of induction motors often degrades under varying operating conditions because labeled fault data are limited and source and target domains may follow different distributions. To address this issue, this paper proposes a Residual Maximum Kernel Discrepancy Adaptation Network (RMKDAN) for cross-condition diagnosis of stator inter-turn short-circuit faults in a three-phase induction motor using the stator current signal measured from one phase. The proposed framework employs a lightweight one-dimensional residual network for hierarchical feature extraction and introduces multi-level Multi-Kernel Maximum Mean Discrepancy (MK-MMD) alignment to reduce distribution discrepancies between source and target domains. A dynamic decay strategy is further applied to the MK-MMD weights to emphasize domain alignment during early training while gradually strengthening discriminative learning. To evaluate the method, an induction-motor fault testbench was constructed, and current signals were collected under different motor frequencies and mechanical loads across eight health conditions. Across the five cross-condition transfer tasks, RMKDAN achieves an average classification accuracy of 77.5%. When the within-domain reference task is also included, the average accuracy across all six diagnostic tasks reaches 81.2%, exceeding DCTLN and FTNN by 4.6 and 5.0 percentage points, respectively. These results demonstrate the effectiveness of RMKDAN for fault diagnosis under changing operating conditions.