Robust Open-Circuit Fault Diagnosis for PMSM Drives Under Unknown Operating Conditions

Data-driven approaches are widely employed for diagnosing open-circuit faults in inverters. However, their performance often deteriorates significantly under unknown operating conditions due to reliance on known training domains. To address this, this paper proposes a cross-domain differential attention network (CDDAN). Firstly, a wavelet principal component feature extraction strategy is introduced. This method precisely locates sensitive frequency bands through wavelet transformation, then employs principal component analysis to compress them into compact, discriminative features, effectively suppressing noise and redundancy. Subsequently, the CDDAN module utilizes a differential attention mechanism to conduct parallel comparative learning across multiple known operating conditions. This mechanism explicitly captures feature differences, thereby extracting key fault characteristics insensitive to operating condition variations. To further enhance the model's generalization capability, a multi-scale differential loss function was designed. This function synergistically optimizes multiple objectives, simultaneously improving diagnostic accuracy and cross-operating-condition adaptability. Experimental results demonstrate that this method can effectively diagnose all 22 open-circuit faults under unknown operating conditions, outperforming existing mainstream approaches.