A sufficient sample size of monitoring data becomes a key factor for describing aircraft engines state. Generative adversarial nets (GAN) can be used to expand the sample size based on the existing state monitoring information. In the paper, a GAN model is introduced to design an algorithm for generating the monitoring data of aircraft engines. This feasibility of the method is illustrated by an example. The experimental results demonstrate that the probability density distribution of generated data after a large number of network training iterations is consistent with the probability density distribution of monitoring data. The proposed method also effectively demonstrates the generated monitoring data of aircraft engine are in a reasonable range. The method can effectively solve the problem of inaccurate performance degradation evaluation caused by the small amount of aero-engine condition monitoring data.