## 1 Introduction  
Permanent magnet-synchronous motors (PMSMs) are increasingly widely used at present, especially in the military and aerospace fields, because of their significant advantages, such as their simple structure, stable operation, large thrust density, and small volume, under the same power as original motors. Nevertheless, the frequent operation of PMSMs under challenging conditions, such as high temperatures, vibration, and dust, increases their susceptibility to failures. The inter-turn fault (ITF) in the stator winding is a prevalent issue encountered in permanent magnet-synchronous motors, which arises from excessive voltage during motor start-up, the degradation of stator winding insulation due to elevated temperatures, and mechanical wear caused by vibrations [1]. Once an ITF occurs in a PMSM, the motor's internal temperature will rise sharply, leading to the demagnetization of the permanent magnet, resulting in a more severe fault of the PMSM. Therefore, it is of great significance to diagnose ITFs in PMSMs. In the context of contemporary research on energy-based maintenance (EBM) and sustainable maintenance (SM), proactive fault prediction and prevention play pivotal roles in mitigating energy consumption and subsequently reducing carbon dioxide emissions and environmental hazards [2]. Given the significant impact of fault prediction and prevention on energy conservation and environmental sustainability, fault diagnosis assumes heightened significance. The original methods of fault diagnosis and identification for ITFs in PMSMs mainly use tools in the field of signal analysis. [PERSON] et al. [3] fused the stator current and the vibration signal between stator teeth and combined the wavelet packet method and fast Fourier transform (FFT) to diagnose the fault. This method has higher reliability than the diagnosis method relying on a single signal. [PERSON] et al. [4] analyzed the DC component and the second harmonic component of the value function in the model predictive control (MPC) system to diagnose the ITFs of PMSMs. This method has higher accuracy than the original method of analyzing the stator current. [PERSON] et al. [5] introduced the Blackman window when performing FFT on fault feature signals, which can quickly identify fault features. However, these methods have significant limitations in diagnosing minor faults. Since [PERSON] et al. [6] first proposed deep learning in 2006, people have paid more and more attention to the application and development prospects of deep learning in fault diagnosis. There are many problems with PMSM fault signals, such as few types, challenges to eliminating random noise, and the significant effects of external interference sources. The available data are very scarce. Given this problem, some scholars use the generative adversarial network (GAN) to expand the sample set to improve the diagnosis results. [PERSON] et al. [7] diagnosed the demagnetization fault of PMSMs based on a GAN and a sparse autoencoder (SAE). This method uses random noise data as the input to generate sample data. [PERSON] et al. [8] diagnosed PMSM ITFs based on the GAN and SAE methods and used random noise data as the input of GAN. However, the original GAN [9] training with random noise as the input is slow, the quality of the generated samples is poor, and pattern collapse occurs easily [10; 11; 12]. Based on the above analysis, this paper proposes an improved diagnosis method of ITFs in PMSMs based on the combination of a Cycle GAN [13; 14; 15] and a DAE [9; 16; 17; 18]. This method abandons the original GAN mode of generating samples with random noise as the input. It uses a PMSM stator three-phase current in healthy and fault states as the input to generate artificial sample sets, uses a DAE to extract fault signals' features, and then uses a Softmax classifier [19] to output the fault types. The experiment shows that this method has a fast training speed and strong robustness and requires few samples. It can identify minor ITFs with an accuracy rate of 98.73%.