Abstract Over the past few decades, recurrent pipeline failures have caused a major impact on human lives and property damage. Studies have shown the lack of a comprehensive, integrated, and accessible set of risk-informed integrity management models and tools for pipeline operators are the main reason behind those damages. To address this gap, this paper presents a system-level Prognosis and Health Monitoring (PHM) modeling framework for gas pipeline system integrity management to prevent or reduce the likelihood of failures. PHM modeling is a comprehensive approach that takes into consideration all possible failure modes of the pipeline under study. It leverages the advancement of sensor technology to stream field data in real-time to perform a dynamic system-level failure analysis based on Hybrid Causal Logic (HCL) and Dynamic Bayesian Networks (DBNs) predictive models to provide cost-effective and optimal mitigation actions such as sensor placement and maintenance schedule optimizations. The developed models are implemented into a software platform where the pipeline operators can observe the real-time and projected health state of the pipeline and the set of suggested actions to enhance the structural integrity of the pipeline system. The platform includes three main modules: Real-Time Monitoring, System-Level Reliability, and Optimal Mitigation Actions. From a safety perspective, the proposed comprehensive and dynamic pipeline health assessment framework either prevents the pipeline failures or reduces their likelihood by supporting pipeline operators in optimal decision-making and planning activities. To verify the performance of the proposed framework and its software implementation, it is applied to a case study of a corroded gas transmission pipeline and the results are discussed.

中文翻译：

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天然气管道系统完整性管理的系统级预测和健康监测建模框架和软件实现

摘要 在过去的几十年里，反复发生的管道故障给人们的生命财产造成了重大影响。研究表明，管道运营商缺乏一套全面、集成和可访问的风险告知完整性管理模型和工具是造成这些损害的主要原因。为了弥补这一差距，本文提出了一种用于天然气管道系统完整性管理的系统级预测和健康监测 (PHM) 建模框架，以防止或减少故障的可能性。PHM 建模是一种综合方法，它考虑了所研究管道的所有可能的故障模式。它利用传感器技术的进步实时流式传输现场数据，以基于混合因果逻辑 (HCL) 和动态贝叶斯网络 (DBN) 预测模型执行动态系统级故障分析，以提供具有成本效益的最佳缓解措施例如传感器放置和维护计划优化。开发的模型被实施到一个软件平台中，管道运营商可以在其中观察管道的实时和预计健康状态以及建议的操作集，以增强管道系统的结构完整性。该平台包括三个主要模块：实时监控、系统级可靠性和最佳缓解措施。从安全角度来说，拟议的综合和动态管道健康评估框架通过支持管道运营商进行最佳决策和规划活动来防止管道故障或降低其可能性。为了验证所提出的框架及其软件实现的性能，将其应用于腐蚀的输气管道案例研究，并对结果进行了讨论。