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…

• NN
  National Natural Science Foundation of China

  Award: U22A2053
• SA
  Science and Technology Department of Guangxi Zhuang Autonomous