[Objective] In modern industrial applications with variable operating conditions and cross-equipment scenarios, the performance of traditional fault diagnosis models for induction motors severely degrades due to data distribution shifts. To address the limitations of existing single-domain alignment strategies (insufficient generalization) and high computational complexity in diagnostic models, this paper proposes a lightweight fault diagnosis method integrating domain adversarial neural network (DANN) with correlation alignment (CORAL). [Methods] A co-simulation platform was established based on Maxwell and Simulink to simulate the dynamic behavior of motors under vector control, and stator current signals under multiple operating conditions were obtained. A preprocessing strategy combining time-domain differencing and continuous wavelet transform was adopted to suppress the fundamental component and generate highly distinguishable time-frequency image features. The lightweight network GhostNetV2 was employed as the feature extraction backbone to reduce computational costs. On the basis of standard domain adversarial training, a CORAL loss was introduced, where the second-order statistics of source and target domain features were explicitly aligned, constructing a dual-domain alignment mechanism that implicit adversarial learning with explicit statistical matching. [Results] The simulation and experimental results demonstrated that the proposed method significantly improved diagnostic performance while maintaining model lightweightness. In the transfer task under varying operating conditions of the same motor, an average classification accuracy of 99.1% was achieved in the target domain. For the more challenging cross-model transfer task between different motors, the average classification accuracy remained at 81.9% in the target domain, which was significantly superior to comparative algorithms. [Conclusion] The proposed dual-domain alignment strategy can alleviate the feature transfer challenges under complex operating conditions to a certain extent, achieving a balanced performance in noise reduction, model lightweighting, and generalization capability. It provides a reliable solution for non-intrusive online monitoring and cross-domain fault diagnosis of induction motors.