Pressure-State Restoration for Early Fault Diagnosis in Gas Pipeline Networks
Urban gas pipeline networks generate continuous monitoring time series from pressure sensors, flow meters, valve states, compressor stations, regulator stations, gas concentration sensors, and customer-demand meters. These signals are noisy because of daily demand fluctuation, regulator adjustment, compressor vibration, sensor drift, weather effects, and telemetry packet loss. Leakage, valve malfunction, regulator instability, and abnormal pressure drops may therefore be masked by normal operational noise. This study develops a diffusion-recovered pressure dynamics model for gas pipeline network anomaly detection. The proposed method reconstructs clean pressure-flow trajectories using a conditional diffusion process constrained by pipeline topology and operating states. A disentanglement module separates demand-driven variation, control-operation fluctuation, and fault-related pressure residuals. Experiments are conducted on a gas network dataset containing 1,280 pipeline zones, 5,640 pressure sensors, 930 flow meters, 460 regulator stations, and 31 monitoring variables collected every 30 seconds over 15 months. The dataset contains 864 million timestamped records and 1,960 verified abnormal episodes, including small leakage, regulator oscillation, valve blockage, compressor instability, and abnormal pressure loss. The proposed method shortens median leakage detection delay from 4.6 hours to 47 minutes compared with a Kalman-smoothed recurrent baseline. False dispatch alerts are controlled at 2.0 cases per pipeline zone per quarter. Diffusion recovery reduces normalized pressure reconstruction error from 0.158 to 0.061, and topology-guided denoising restores 23.4 million incomplete pressure windows during evaluation. Full city-level assessment is completed in 13.5 minutes with median scoring latency of 45 ms per zone window. These findings indicate that diffusion-guided signal recovery can improve robust anomaly detection in noisy gas pipeline operation time series.