System-level prognosis and health monitoring modeling framework and software implementation for gas pipeline system integrity management

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.