## 4 Gas Turbine Test Bench Experiment and Fault Diagnosis
### Results Visualization Comparison  
In order to verify the performance of the proposed method, the gas turbine samples are generated by GAN, DCGAN, and Improved DCGAN to form new datasets: dataset with original samples, dataset including generated samples by GAN, dataset including generated samples by DCGAN, and dataset including generated samples by Improved DCGAN. The four datasets are selected to train the classification model and classify the faults with different methods. Table 6 and Figure 18 display the classification accuracy comparisons of different diagnostic methods with the four datasets. When SVM is adopted, the classification accuracies of the dataset with original samples, the dataset including generated samples by GAN, the dataset including generated samples by DCGAN and the dataset including generated samples by Improved DCGAN are 88.25%, 91.05%, 91.9%, and 92.15%, respectively; when LSTM is used, the classification accuracies of the four datasets are 90.05%, 92.25%, 94.3%, and 96.83%, respectively. When DBN and STFT-CNN are used, the classification results greatly improved. It can be seen that the classification effect by WDCNN performs best under each dataset compared with other methods. Meanwhile, it can be seen that the classification accuracy of the dataset with original samples, the dataset including generated samples by GAN, the dataset including generated samples by DCGAN, and dataset the including generated samples by Improved DCGAN are 92.1%, 96.4%, 97.9%, and 98.65%, respectively. It can be concluded that the classification accuracy of the dataset including generated samples by the Improved DCGAN method obviously increased compared with the dataset including generated samples by GAN and the dataset including generated samples by DCGAN. For the sake of displaying the classification results of the fault datasets by these methods, the visualized t-distributed Stochastic Neighbor Embedding (t-SNE) diagrams [51] are shown in Figure 19. Purple dots represent normal samples, blue dots represent airflow excitation fault samples, green dots are unbalanced fault samples, and yellow dots are misalignment fault samples. It can be seen in Figure 19a that the original samples without generated samples are mostly classified, unbalanced fault samples and misalignment fault samples are gathered together and not well separated, and some airflow excitation faults are misclassified. Figure 19b displays the classification results of the dataset including gen \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Accuracy for Different Datasets**} \\ \cline{2-5}  & **Original Samples** & \begin{tabular}{c} **Including Generated** \\ **Samples by GAN** \\ \end{tabular} & \
\begin{tabular}{c} **Including Generated** \\ **Samples by DCGAN** \\ \end{tabular} &
\begin{tabular}{c} **Including Generated** \\ **by Improved DCGAN** \\ \end{tabular} \\ \hline SVM & 88.25\% & 91.05\% & 91.90\% & 92.15\% \\ \hline LSTM & 90.05\% & 92.25\% & 94.30\% & 96.83\% \\ \hline DBN & 91.25\% & 94.95\% & 95.15\% & 98.03\% \\ \hline STFT-CNN & 91.95\% & 96.25\% & 97.45\% & 98.08\% \\ \hline WDCNN & 92.10\% & 96.40\% & 97.90\% & 98.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different diagnostic methods for different datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Precision**} \\ \cline{2-5}  & **Normal** &
\begin{tabular}{c} **Airflow** \\ **Excitation** \\ \end{tabular} & **Unbalanced** & **Misalignment** & **Accuracy** \\ \hline DBN & 98.90\% & 98.40\% & 97.80\% & 97.00\% & 98.03\% \\ SVM & 92.50\% & 94.60\% & 90.30\% & 91.30\% & 92.15\% \\ STFT-CNN & 97.30\% & 97.80\% & 98.40\% & 98.80\% & 98.08\% \\ WDCNN & 99.80\% & 98.10\% & 98.30\% & 98.40\% & 96.65\% \\ LSTM & 96.80\% & 98.60\% & 93.20\% & 98.70\% & 96.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different diagnostic models. erated samples by GAN; it can be seen that the classification results improved.