Research on Gas Pipeline Leakage Prediction Model Based on Physics-Aware GL-TransLSTM.

Area of Science:

• Artificial Intelligence
• Biomimetic Systems
• Environmental Monitoring

Background:

• Natural gas pipeline leak monitoring faces challenges from environmental noise and complex signals, hindering prediction accuracy.
• Existing methods struggle with non-stationary data and multi-source variable couplings, limiting robustness.
• Biological systems offer insights into multimodal integration and dynamic attention for improved perception.

Purpose of the Study:

• To propose GL-TransLSTM, a novel biomimetic hybrid deep learning model for enhanced natural gas pipeline leak monitoring.
• To leverage synergistic integration of Transformer and LSTM architectures inspired by biological perceptual systems.
• To improve prediction accuracy and robustness in challenging industrial environments.

Main Methods:

• Developed GL-TransLSTM, a hybrid model combining Transformer's global self-attention and LSTM's gated memory.
• Implemented a multimodal fusion pipeline with CEEMDAN for multi-scale feature extraction.
• Incorporated a physics-informed gated attention mechanism and adaptive sliding window for temporal granularity.

Main Results:

• GL-TransLSTM achieved high performance on an industrial dataset, with 99.93% accuracy, 99.86% recall, and 99.89% F1-score.
• The model significantly outperformed conventional LSTM and Transformer-LSTM baselines.
• Demonstrated enhanced modeling capacity and generalization for non-stationary signals in noisy environments.

Conclusions:

• The proposed biomimetic framework offers a substantial advancement in natural gas leak detection.
• Synergistic fusion of multi-scale features, physics-guided learning, and bio-inspired architecture enhances performance.
• GL-TransLSTM provides a robust solution for monitoring in complex industrial settings.