Aiming To address the problems of limited sample size, uneven sample distribution, and cross-operating condition fault diagnosis for gas turbine rotor systems, a fault diagnosis method based on the linkage of numerical model and physical model is proposed. A classical central mass method is used to establish the dynamic model of the rotor system, and fault dynamics differential equations are established in the model by introducing misalignment fault and unbalance fault. Finally, the differential equations of rotor system fault dynamics are solved by the Runge-Kutta method (ode-4/5), and the simulated signal of fault displacement is obtained, which is prepared for subsequent data augmentation and model linkage methods. A conditional deep convolutional generative adversarial network (GAN) model is established by combining the orthogonal gradient penalty algorithm, and the model is used to generate signals by inputting the simulated signals obtained from the physical model as the generator input, and inputting the real experimental signals as the discriminator input to obtain a generated signal that integrates the intrinsic characteristics of the physical model and the actual mechanical characteristics. Secondly, a cross-operating condition domain adaptation fault diagnosis model is established based on the theory of transfer learning, and the data is converted into a two-dimensional temporal-frequency image sample using a combined short-time fractional Fourier transform and inverse convolution algorithm, which provides more obvious resolution and features. The data that integrates the intrinsic characteristics of the physical model and the mechanical characteristics is used as the source domain and the other target domain data to be measured at other operating conditions is used to train the fault diagnosis model. The experimental verification shows that the diagnostic accuracy of the five different fault categories at different speeds and fault levels in different operating conditions is all above 91%. The results are explained and analyzed through confusion matrix, and the method can effectively improve the model's generalization ability and realize cross-operating condition fault diagnosis of rotor systems, which is superior to other mainstream diagnosis methods under the same conditions.