Aiming at the problem that when applying deep learning for gas turbine fault diagnosis, the fault signal data is difficult to obtain, resulting in many normal operation samples and few fault samples, which affect the accuracy of fault diagnosis. A method for augmenting gas turbine fault samples using deep convolutional generative adversarial learning is proposed. According to the characteristics of the gas turbine vibration signal, the fault signal is preprocessed by using fast Fourier transform, empirical mode decomposition and demodulation, and the fault frequency domain features are extracted and the eigenvalue index is selected, and the vibration signal is converted into a two-dimensional gray image. The orthogonal gradient penalty algorithm is used to train the deep convolutional generative adversarial fault sample generation model. The example results show that the test accuracy rate of CWRU bearing dataset obtained is 98. 01%, and the test accuracy rate of a certain type of gas turbine’s fault samples generated by the proposed method is 97. 43%, which are better than other mainstream fault sample generation methods under the same conditions. The effectiveness and superiority of the proposed fault sample generation method are verified.