Systematic Integration of PHM and PRA (SIPPRA) for Risk and Reliability Analysis of Complex Engineering Systems

Abstract

Complex Engineering Systems (CES) such as power plants, process plants, and manufacturing plants have numerous, interrelated, and heterogeneous subsystems with different characteristics and risk and reliability analysis requirements. With the advancements in sensing and computing technology, abundant monitoring data is being collected. This is a rich source of information for more accurate assessment and management of these systems. The current risk and reliability analysis approaches and practices are inadequate in incorporating various sources of information, providing a system-level perspective, and performing a dynamic assessment of the operation condition and operation risk of CES.

In this dissertation, this challenge is addressed by integrating techniques and models from two of the major subfields of reliability engineering: Probabilistic Risk Assessment (PRA) and Prognostics and Health Management (PHM). PRA is very effective at modeling complex hardware systems, and approaches have been designed to incorporate the risks introduced by humans, software, organizational, and other contributors into quantitative risk assessments. However, PRA has largely been used as a static technology mainly used for regulation. On the other hand, PHM has developed powerful new algorithms for understanding and predicting mechanical and electrical device health to support maintenance. Yet, PHM lacks the system-level perspective, relies heavily on operation data, and its outcomes are not risk-informed.

I propose a novel framework at the intersection of PHM and PRA which provides a forward-looking, model- and data-driven analysis paradigm for assessing and predicting the operation risk and condition of CES. I operationalize this framework by developing two mathematical architectures and applying them to real-world systems. The first architecture is focused on enabling online system-level condition monitoring. The second architecture improves upon the first and realizes the objectives of using various sources of information and monitoring operation condition together with operational risk.