To address the issues of scarce fault data for varying blockage levels of intake or exhaust system in aero piston engine, and the resultant unbalanced sample sizes that lead to poor diagnostic performance and low robustness, this paper defined two experimental scenarios: fault diagnosis under small-sample conditions and fault diagnosis under class-imbalance conditions. A Transfer-Architecture-based classconditional Wasserstein GAN with gradient penalty (TCWGAN-GP) was proposed to generate high-quality multi-source fault samples of specified categories. The generator of TCWGAN-GP was based on the encoder of Vision Transformer as the backbone network to fully capture the corresponding relationships among different block data sources. The loss function combines the Wasserstein distance and the gradient penalty term GP to prevent model collapse and gradient vanishing, thereby enhancing the stability of adversarial training. The screened and generated samples were merged with the original data for training the diagnostic model to verify the quality of the samples. Experiments were conducted under two stable operating conditions across the two defined scenarios. The average test accuracy was improved to varying degrees compared to the original dataset. For example, in the class-imbalanced experiment of the 1 750 r/min_50% throttle dataset, the average test accuracy increased by 55.74% and 59.26% when the training rounds were 30 and 50, respectively. In the ablation experiment, the samples generated by the proposed method were closer to the real samples, achieving an accuracy rate of 100% in the diagnostic test, Its test accuracy and robustness were superior to other generation methods.