UCLA

Recurrent pipeline failures continue to be a source of safety and economic risk related to processing, transporting, and distributing natural gas. Studies have shown the lack of comprehensive, integrated, and accessible risk-informed integrity management models and tools for pipeline operators is a major contributor. To address this gap, this research 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. The proposed PHM approach 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) including a Dynamic Bayesian Network (DBN) corrosion model, to provide cost-effective and optimal mitigation actions such as sensor placement and maintenance schedule optimizations. The developed models are implemented in 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 Health Monitoring, System-Level Reliability, and Optimal Mitigation Actions. From a safety perspective, the proposed PHM can prevent pipeline failures or reduces their likelihood by supporting pipeline operators in optimal decision-making and planning activities. To demonstrate potential benefits and performance of the proposed framework and software implementation, it is applied in a case study involving a corroding gas transmission pipeline.