## 4 Gas Turbine Test Bench Experiment and Fault Diagnosis
### The Generated Data and Results Comparison  
The original signals and generated fault sample signals under four states by the proposed method are demonstrated. Figure 14 shows the generated sample signals of the gas turbine rotor system under the normal state. Figures 15 and 16 show the generated sample signals of the gas turbine rotor system under the airflow excitation state and the unbalanced state. Figure 17 shows the generated sample signals of the gas turbine rotor system under the misalignment state. For comparison, the generated fault sample signals by GAN and DCGAN are also displayed. Figure 16: Signal comparison of time domain and frequency domain of the original signals and the generated signals in unbalanced state. Figure 15: Signal comparison of time domain and frequency domain of the original signals and the generated signals in airflow excitation state. Meanwhile, the original signals and generated signals based on GAN, DCGAN, and the Improved DCGAN method are analyzed by frequency domain and time-frequency domain spectrum analysis. In order to better verify the similarity between the generated samples and the original samples, spectrum analysis and Structural Similarity (SSIM) analysis are used for verification. (1) Spectrum analysis  
The similarities between the generated samples and the original samples are analyzed by time domain analysis, Fast Fourier Transform (FFT) spectrum analysis, Short-Time Fourier Transform (STFT) spectrum analysis, and Continue Wave Transform (CWT) analysis. It can be seen from the comparison of spectrum signals in normal states in Figure 14 that the spectrum of the newly generated sample signals is consistent with the spectrum of real sample signals. It can be clearly seen from the FFT and CWT diagrams that the samples generated by the proposed method have the highest similarity with the original signals. The generated samples by GAN and DCGAN are similar to the original samples, but not completely equivalent, and there are small deviations at some peaks. Figure 15 shows the vibration signals and generated sample signals under airflow excitation from time domain signals, frequency domain, and time-frequency spectrum. It can be seen from the FFT spectrum diagram of the original signals that there are multiple doubling frequency components in the frequency domain, including fundamental frequency f0, double frequency 2f0, and triple frequency 3f0. Meanwhile, it can be seen from the FFT spectrum diagram of generated samples by the proposed method that these frequency features are similar to the features of original signals under air flow excitation, which proves that the key features of the samples generated by the proposed method are consistent with those of original samples, and the quality of the generated samples is high. The samples generated by GAN have no obvious multi-frequency components in the FFT diagram, and the samples generated by DCGAN have no obvious multi-frequency components in the CWT diagram. By the method, the generator can generate new airflow excitation samples with a similar distribution to the original samples, which are most con  
Figure 17: Signal comparison of time domain and frequency domain of the original signals and the generated signals in misalignment state. sistent with the original signals. With this method, the training samples can be expanded, and the training of the classifier can be enhanced to further improve the generalization performance of the model. Figure 16 shows the generated gas turbine rotor fault sample signals under the unbalanced state, it can be seen that the generated samples by the proposed method had higher similarity with the original signals than those of GAN and DCGAN. Figure 17 shows the generated gas turbine rotor fault sample signals under the misalignment state generated by GAN, DCGAN, and Improved DCGAN.