Digital Twin Driven Condition Monitoring for Gas Pipeline Network 核心 · 已核验

mxdseget3rwd4g02

"Abnormal conditions in urban gas pipeline networks can readily cause environmental pollution, personal injury, and property damage. Timely detection of such anomalies is critical for accident prevention. However, the increasing scale and intricacy of urban gas pipeline networks pose substantial challenges for their condition monitoring. While the concept of digital twins in gas pipeline network has been explored in some studies, there is still lacking of a comprehensive technical framework to effectively support the pipeline network condition monitoring. Therefore, this work develops Gas Pipeline Network Digital Twin (GPN-DT) system for high-precision condition monitoring of gas pipeline network. The contributions are twofold: 1) A topology-driven modular pipeline network model is established to preserve the hydraulic characteristics of the system while reducing modeling complexity through structural decoupling based on conservation principles.} (2) An adaptive process-driven network model update mechanism is proposed to enable dynamic tracking of the pipeline network, thereby achieving effective condition monitoring of the gas pipeline network. A case study is conducted based on an experimental platform for pipeline network condition monitoring. The results validate the effectiveness and feasibility of the proposed GPN-DT system for the condition monitoring of gas pipeline network. The practical value of the proposed GPN-DT system lies in its ability to enhance real-time situational awareness, support early anomaly detection of potential risks, and improve the safety and efficiency of urban gas pipeline operations."

本卡描述如含来自官方页的原文片段,其版权归原作者,不在本站 CC-BY 4.0 许可范围(见关于与许可)。

落地页

https://ieee-dataport.org/documents/digital-twin-driven-condition-monitoring-gas-pipeline-network

国内可访问性

国内直连:可达 (2026-07-11 检测) 非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

PHM 任务

condition_monitoring anomaly_detection