Leak Prediction Method for Gas Pipelines Based on an Improved Transformer

The prediction of gas pipeline leakage is not only of great significance to public safety and environmental protection but also plays a crucial role in economic benefits, management efficiency, and social responsibility. This paper introduces an improved Transformer-based method for gas pipeline leakage prediction. This method leverages the unique advantages of the Transformer model's self-attention mechanism, enabling efficient handling of dependencies in long time series and capturing complex relationships between multidimensional data. The proposed improvements enhance the Transformer model's generalization ability, robustness, and accuracy when dealing with multivariate time series data. During training, adversarial samples were generated for adversarial training to increase the model's robustness against minor perturbations; structured anomalies were added to the data, and regularization loss was calculated to prevent overfitting. Experiments based on real multidimensional sensor data demonstrate that the proposed model significantly improves the accuracy of gas pipeline leakage prediction.