Fault diagnosis of aero-engines is of great significance to flight safety and operational efficiency. However, in practical applications, large-scale labeled fault data are difficult to obtain, leading to overfitting and insufficient generalization ability of traditional models under small-sample conditions. To address this challenge, this study introduces an advanced tabular generation model for sensor data synthesis, aiming to achieve data augmentation in small-sample scenarios. The constructed tabular generation model is extended from a tabular prior fitting network. It can quickly fit data distributions without additional training, and exhibits higher efficiency and adaptability compared with existing tabular generation methods. The prediction model is trained and evaluated using UniXDE.DTM, which is an advanced data modeling software. Experimental results show that the proposed method can effectively expand aero-engine fault samples, significantly improve diagnostic performance under small-sample conditions, and achieve optimal results in comparison with various mainstream generation models. This research provides an efficient and feasible solution for small-sample fault diagnosis, and demonstrates the application potential of large tabular models in the intelligent operation and maintenance of key industrial equipment.