## 1 Introduction
In the method based on model analysis, sensors are used to monitor each component of the engine, signal processing is performed, and engine mathematical models, statistical analysis models, or artificial intelligence pattern recognition models based on observation data are established. In the data-driven methods, mathematical models, statistical analysis models, and machine learning models are integrated, and expert experiences, knowledge about engines, and information such as engine monitoring data are merged. Among such methods, the intelligent diagnosis method uses many new methods and means, including fuzzy logic, expert system, and artificial neural network [11]. With the continuous application development and improvement of these methods, the condition monitoring of mechanical equipment will gradually be systematic and intelligent. At present, data-driven diagnostic methods mainly include statistical analysis methods, signal processing methods, and artificial intelligence-based methods [12]. Among them, statistical methods are limited to statistical analysis theories, and their scope of application is limited. Signal processing technology is an important means of mechanical vibration fault analysis, represented by Fourier transform [13], wavelet packet analysis [14], Hilbert-Huang transform [15], and other methods. The signal processing method is based on the signal analysis technology to extract the time-frequency domain characteristic parameters and characterize system state, and data loss still occurs in this process [16]. The fault diagnosis method based on artificial intelligence technology does not require the establishment of professional related mathematical models, and the application of advanced classification and regression algorithms to historical data or running logs can realize fault diagnosis [17]. In many fault diagnosis methods based on artificial intelligence technology, typical representatives are methods based on artificial neural networks [18], methods based on support vector machines [19], and methods based on fuzzy logic [20]. At the same time, the classic classification methods of machine learning also include decision trees, random forests, and extreme decision trees. With the breakthrough in the performance of artificial intelligence algorithms, many advanced data processing methods, image recognition tools, and data analysis algorithms have gradually been applied in the prototype design, method optimization, and fault diagnosis of gas turbines [21; 22; 23; 24], and many results have been achieved. The progress of deep learning also has some applications in gas turbine fault diagnosis [25; 26; 27]. However, most of the intelligent diagnosis methods based on deep learning require sufficient data samples, and the problem of fault data scarcity in the actual engineering of gas turbines is a big challenge for gas turbine application. At present, due to the complex structure of the large gas turbine rotor system and the limited test conditions, there are only some fault data under each working condition of practical running, and some faults have never occurred. For the fault types not involved in the sample set, it is difficult to obtain accurate diagnosis results by using deep learning methods. In addition, individual differences such as equipment manufacturing and installation errors and changes in operating conditions make the application scenarios of equipment complex and changeable; thus, the applicability of the method needs to be improved. Therefore, how to solve the problems of a small sample, insufficiently labeled data, and low diagnosis accuracy has become the top priority in fault diagnosis and identification. Generative Adversarial Network (GAN) as an artificial intelligence method provides a good technical means to solve this problem [28; 29]. Its unique adversarial idea makes it stand out among many generator models, and it has wide applications in sample generation, data enhancement, and other fields.