## 5 Conclusions  
In this paper, two learning models of Deep Convolutional Generation Confrontation Network (DCGAN) and Convolutional Neural Network (CNN) are used to complete the fault diagnosis of induction motors for solving the problem of unbalanced training data. First of all, four signal processing methods are compared on the actual and balanced dataset. Time-frequency analysis has the advantage of fault diagnosis under different loads, and the accuracy rate measured by using the low-load side as the training set is higher, which can reach 95.06% and 96.38%. It is significantly better than single signal processing. When the training data are unbalanced, the more unbalanced the training data, and the lower the accuracy of the obtained test set under any signal pre-processing. It shows that the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Method** & **Training Set** & **Testing Set** & **Accuracy Ratio** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Condition1: \\ Balanced data \\ (Actual data) \\ \end{tabular} } & 2T-CNN & H1 & 71.83\% \\  & FFT-CNN & H1 & 82.21\% \\  & STFT-CNN & H1 & 95.06\% \\  & WT-CNN & H1 & 96.38\% \\ \hline \multirow{4}{*}{\begin{tabular}{c} Condition2: \\ Imbalanced data \\ (Add generated data) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} STFT-GAN-CNN \\ (Add generated data) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} C1-expand \\ C2-expand \\ C3-expand \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} 84.25\% \\ 77.6\% \\ 73.93\% \\ \end{tabular} } \\ \cline{2-3}  & WT-GAN-CNN & & & 83.03\% \\  & WT-GAN-CNN & C2-expand & 79.07\% \\  & WT-GAN-CNN & C3-expand & 78.68\% \\ \hline \multirow{4}{*}{\begin{tabular}{c} Condition3: \\ Imbalanced data \\ (Different CNN models) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} STFT-GAN-CNN \\ (ResNet50) \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} C1-expand \\ C2-expand \\ C3-expand \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} F2 \\ 82.27 \\ 80.63 \\ \end{tabular} } \\ \cline{2-3}  & STFT-GAN-CNN & C1-expand & 93.28\% \\  & (VGG16) & C2-expand & 91.52\% \\  & (VGG16) & C3-expand & 82.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of accuracy of data augmentation technology. data-driven fault diagnosis method is susceptible to the impact of the unbalanced training dataset. Therefore, the DCGAN-generated data is about 80% similar to the real examples in addition to the actual samples. After over-sampling the unbalanced data, the classification accuracy can be increased to more than 90%, which is closer to the accuracy of the actual balanced dataset, and the over-sampling-balanced (Pro-Balanced) method is better. In addition, if the parameters of DCGAN can be adjusted effectively, the generated samples can be better than image processing technology, but the training time will be longer. In the cross-load test, we can obtain up to about 85% of the diagnosis results after adding the unbalanced data to the generated data. It shows that the generated data have learned different fault characteristics, and better diagnostic results can be obtained through a deeper CNN model. It also highlights the ability of DCGAN to learn the characteristics of the input signal. Finally, after the case analysis of this study, it is confirmed that the Deep Convolution Generative Adversarial Network (DCGAN) can improve the impact of the imbalance of training data. However, in the application of actual fault diagnosis, most of them still hope to find the potential fault of the induction motor and repair it as soon as possible. This research can only reach the classification diagnosis after the induction motor fails, so the technology has yet to breakthrough. In addition, unlabeled data still account for the majority of collected data. Therefore, in the future, semi-supervised learning technology can be introduced, and the useful information can be extracted from labeled data and unlabeled data, which will improve the usability of diagnostic models.