The monitoring data of permanent magnet synchronous motor (PMSM) exhibit complexities such as non-smoothness

non-linearity

multi-source heterogeneity and low value density. These characteristics make it challenging to accurately model the type and extent of motor faults using simulation data. The serious imbalance between normal and faulty data samples leads to problems such as overfitting and low accuracy in the training of fault diagnosis models. In this paper

an improved auxiliary classification generation adversarial network (ACGAN) is proposed to study the expansion of real fault data for PMSM by learning the distribution characteristics of the original samples

while the generated fault dataset provides a data base for the next fault diagnosis and health assessment. Firstly

to address the problems of poor convergence and the tendency for gradients to disappear or explode in ACGAN networks

the Wasserstein distance is used to constrain the reconstruction loss of the generated data

and the gradient penalty is used instead of weight clipping to optimize the model and mitigate model training instability. Secondly

to analyze the change relationship between data and the historical change pattern

recurrent neural network is introduced in the generator to improve the quality of the generated data. Finally

the effectiveness of four data expansion methods

ROS

SMOTE

ADASYN and improved ACGAN

is compared and analyzed in improving the performance of fault diagnosis models using fault data from PMSM inter-turn short circuits. Results show that the model trained using the improved ACGAN method is more stable

converges faster and produces expanded data of superior quality than those adopting other data expansion methods.