## 4 Gas Turbine Test Bench Experiment and Fault Diagnosis
### Classification Model and Results  
After new samples are generated, the original samples and generated fault samples form a new dataset. The new gas turbine sample dataset is divided into a training dataset and a testing dataset. The details of the datasets are shown in Table 3. The original training dataset has 2100 samples for fault diagnosis, the training dataset including generated samples has 4000 samples, and the testing dataset has 2000 samples. In order to examine the superiority of the proposed method, several fault diagnosis methods including Support Vector Machine (SVM), Deep Belief Network (DBN) [49], Two-Dimensional Convolutional Neural Network with Short-Time Fourier Transform of the signals as input (STFT-CNN), WDCNN, and Long Short-Term Memory (LSTM) [50] are adopted for diagnosis classification comparison of the target data. SVM is proposed on the basis of statistical learning theory and used in mechanical fault diagnosis because of its faster training convergence rate and stronger generalization ability. DBN saves the process of manually extracting features and fuses feature extraction and classification together. STFT-CNN adopts the Short-Time Fourier Transform (STFT) of the obtained sample signals as input and the Two-Dimensional Convolutional Neural Network is used for fault classification and analysis. WDCNN is more suitable for one-dimensional vibration signals with high classification accuracy, and the parameter settings of WDCNN are the same as those in Table 1. LSTM has strong adaptability for time series data and is widely used in prediction and classification.  
Table 5 shows the specific classification results of each fault by different diagnosis methods. Precision refers to the ratio of the number of correctly predicted positive samples to the number of all predicted positive samples. When SVM is used to classify new samples, the precision of normal, airflow excitation, unbalanced, and misalignment states are 92.50%, 94.60%, 90.30%, and 91.30%, respectively, and the classification accuracy is 92.15%. The classification accuracy of DBN and STFT-CNN improved to 98.03% and 98.08%. When WDCNN is used to classify new samples, the classification precision for normal, airflow excitation, unbalanced, and misalignment states are 99.80%, 98.1%, 98.30%, and 98.40%, respectively, and the classification accuracy is 98.65%. From the classification results, it can be seen that WDCNN is suitable for gas turbine rotor fault classification, and WDCNN was thus chosen as the classification model for gas turbine rotor fault classification. The proposed Improved DCGAN model generates higher-quality data, and the new dataset can be used for further fault classification. The WDCNN accurately learns the data distribution and features of different states of the gas turbine, and can provide higher diagnosis accuracy for the gas turbine rotor system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Faults**} & \multicolumn{3}{c}{**STFT**} & \multicolumn{3}{c}{**CWT**} \\ \cline{2-7}  & **GAN** & **DCGAN** & **Improved DCGAN** & **GAN** & **DCGAN** & **Improved DCGAN** \\ \hline Normal & 0.8699 & 0.9502 & 0.9937 & 0.7884 & 0.9456 & 0.9660 \\ Airflow excitation & 0.8522 & 0.9482 & 0.9937 & 0.7874 & 0.9368 & 0.9885 \\ Unbalanced & 0.8576 & 0.9403 & 0.9955 & 0.7959 & 0.9345 & 0.9683 \\ Misalignment & 0.8412 & 0.9214 & 0.9944 & 0.7976 & 0.9421 & 0.9683 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SSIM comparisons on between generated signals and real signals under different states.