## 1 Introduction
In the Generative Adversarial Network, the existence of the generator means that the sample size and completeness are no longer the key factors that affect the accuracy of the entire network. [PERSON] et al. [30] proposed a new generative adversarial learning model to deal with the fault diagnosis of rotating machinery. The generator can generate fault data with higher reliability to improve the fault diagnosis dataset. [PERSON] et al. [31] aimed at the problem of sample imbalance, obtained the fault characteristics of the signal through GAN, and generated new fault samples. [PERSON] et al. [32] proposed a generative model based on Generative Adversarial Networks to generate synthetic samples that conform to the original data sample distribution, and extended training samples to solve the overfitting problem of the classification model due to insufficient training samples. [PERSON] et al. [33] used the Enhanced Generative Adversarial Network to compare the probability distribution between the generated signals and the real signals, and generated the fault signals for rotating machinery fault diagnosis. The Deep Convolutional Generative Adversarial Network (DCGAN) is an improvement model of GAN [34]. It mainly improves the network structure of GAN. It combines the automatic feature extraction layer of the sample with the GAN network, which greatly improves the stability of GAN training and the quality of generated data [35; 36]. However, the signal generator of the traditional GAN or DCGAN generates the sample signals based on random data. The random noises generated each time during the operation comprise a special code that only a group of generators can understand, and this code determines how the generated results should be represented. The result generated by using random data as the encoding source has little relation with the original signals, making the generator prone to producing meaningless output, resulting in unstable network training and learning, and low quality of the generated signals. Therefore, an improved method based on the original DCGAN to generate sample data is proposed and applied to gas turbine rotor system fault diagnosis. Firstly, the proposed method combines the data generation ability of DCGAN and the feature extraction ability of Convolutional Neural Network (CNN) to construct a new optimized signal generator, which further improves the quality of generative fault samples for fault diagnosis. Secondly, generative samples are combined with original samples to form feasibility samples and supplement the gas turbine rotor fault dataset, and then the fault samples are adopted to build up the fault diagnosis model. Finally, gas turbine test bench experiments are carried out for fault classification and to verify the proposed method. The main contributions can be summarized as follows:1. The Improved DCGAN is proposed, and the generator is optimized by adding a one-dimensional Deep Convolutional Neural Network With a Wide First-Layer Kernel (WDCNN) to the deconvolution layer of the generator, to build a generator containing the gas turbine vibration data features and generate samples with higher quality and similarity to original samples. 2. The loss function of the generator and discriminator are optimized, and a gradient penalty is adopted to solve the problem of gradient disappearance and improve the applicability of the method. The optimized penalty factor can be adjusted according to the actual situation, which improves the adaptability of the method. 3. The generated effective fault samples are added to the fault dataset, and the proposed method is validated on the gas turbine rotor system fault data to solve the problem of insufficient gas turbine rotor system fault samples and provide new ideas for gas turbine rotor system fault diagnosis. The remainder of this paper proceeds as follows. Section 1 is the introduction, Section 2 presents theoretical method, Section 3 is the proposed fault diagnosis method, Section 4 is the experiment and results, and Section 5 concludes the paper.