## 5 Conclusions  
Based on the sample characteristics of PMSM inter-turn short-circuit faults, this study reconstructed the Cycle GAN network. Furthermore, the collected samples of permanent magnet-synchronous motor inter-turn faults were expanded. DAE was utilized for feature extraction, while the Softmax classifier was employed for fault label classification and diagnosis. In the experimental section, diagnoses were conducted for five different inter-turn short-circuit fault states of four motors under three operating conditions, with an average \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Motor** & **Number of Samples** & **Expansion Method** & **Accuracy** \\ \hline  & 4500 & nothing & 89.81\% \\
1 & 4500 & GAN & 93.87\% \\  & 4500 & Cycle GAN & 98.73\% \\ \hline  & 4500 & nothing & 88.92\% \\
2 & 4500 & GAN & 91.19\% \\  & 4500 & Cycle GAN & 98.92\% \\ \hline  & 4500 & nothing & 89.23\% \\
3 & 4500 & GAN & 92.71\% \\  & 4500 & Cycle GAN & 98.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of fault diagnosis accuracy before and after sample set expansion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Samples** & **Classification Model** & **Expansion Method** & **Accuracy** \\ \hline
4500 & BP\&Softmax & GAN & 66.73\% \\
4500 & SVM\&Softmax & GAN & 83.19\% \\
4500 & CNN\&Softmax & GAN & 87.96\% \\
4500 & SAE\&Softmax & GAN & 93.41\% \\
4500 & DAE\&Softmax & Cycle GAN & 98.84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy of similar methods. accuracy rate of 98.84%. This method demonstrates strong generalization capabilities, effectively detecting faults and their severity across various motor types, loads, or speeds. In Section 3, this study optimized the network's hyperparameters through trial and error, revealing that using LeakyRelu as the activation function for hidden layers and Tanh as the activation function for the output layer was most appropriate. Employing the 2-b layer combination from Table 1 endowed the network with both strong stability and generalization, with the lowest loss function value observed under this structure. Subsequent experimental results further validated the effectiveness of this network configuration. The experimental results indicate that samples generated by Cycle GAN exhibit a higher correlation with the collected samples compared to other methods. Additionally, the details, such as random noise and current fluctuations in these generated samples, align more closely with real-world scenarios, rendering them more practical in fault diagnosis. It has been observed that extending the original dataset using Cycle GAN improves the fault diagnosis accuracy of the DAE and Softmax models by 6%. Compared to similar methods, our proposed Cycle GAN-DAE model demonstrates outstanding performance in fault diagnosis tasks. In comparison to the original methods, deep learning techniques have gained significant popularity in motor fault diagnosis due to their ability to effectively express crucial data. Our designed sample generation network addresses the challenges associated with deep learning-based fault diagnosis methods, providing valuable insights for future research endeavors, which heavily rely on high-quality datasets that may be challenging to collect specifically for permanent magnet-synchronous motor faults. Furthermore, existing methods utilizing neural networks for diagnosing faults in permanent magnet-synchronous motors are all offline diagnoses. We aim to address this issue in future research to achieve an online fault diagnosis of permanent magnet-synchronous motors, which holds significant research value and potential for industrial applications. On the contrary, this proposed method exhibits certain limitations. Firstly, the establishment of a deep model is relatively intricate, resulting in high modeling costs and limited timeliness.