A Diffusion Model-Empowered CNN-Transformer for Few-Shot Fault Diagnosis in Natural Gas Wells

Abstract

Natural gas wells operate under complex conditions with frequent environmental disturbances. Fault types vary significantly and often present weak signals, affecting both safety and efficiency. This paper proposes an intelligent fault-diagnosis method based on a CNN-Transformer model using real-time wellsite data. A time series diffusion model is applied to enhance small-sample data by generating synthetic fault samples, and the CNN-Transformer model extracts both local and global features from time series inputs to improve fault recognition in complex scenarios. Validation on a real-world dataset demonstrates that the proposed method achieves a macro F1-Score of 99.52% in multi-class fault diagnosis, significantly outperforming baseline models (1D-CNN: 95.83%, LSTM: 93.54%, GRU: 94.98%). Quantitative analysis confirms the diffusion model’s superiority in data augmentation, with lower Earth Mover’s Distance (0.087), KL Divergence (0.245), and Mean Squared Error (0.298) compared to GAN and VAE variants. Ablation studies show that removing diffusion-based augmentation leads to a 14.96% drop in F1-Score, highlighting its critical role in mitigating class imbalance. Results validate the diffusion model’s effectiveness for data augmentation and the CNN-Transformer’s superior ability to capture complex time series patterns, providing theoretical support and practical tools for intelligent monitoring and maintenance in natural gas well systems.