Systematic Integration of PHM and PRA for Risk and Reliability Analysis of Complex Engineering Systems
Author: Ramin Moradi
Date/Time: Wednesday, June 16th. | 2 pm – 4 pm
Dr. Katrina Groth, Chair/Advisor
Dr. Mohammad Modarres
Dr. Enrique Lopez Droguett
Dr. Michelle Bensi
Dr. Shapour Azarm
Dr. Greg Baecher, Dean’s Representative
“Complex Engineering Systems (CES) such as power plants, process plants,
manufacturing plants, etc. have numerous, interrelated, and heterogeneous subsystems with different characteristics and risk and reliability analysis requirements. On the other hand, with the advancements in sensing and computing technology, abundant monitoring data is being collected which is a rich source of information for a 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, which are 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 and in the design stage of the systems. On the other hand, PHM has developed powerful new algorithms for understanding
and predicting mechanical and electrical devices’ health. Yet, PHM lacks the system-level perspective, relies heavily on the operation data, and its outcomes are not risk-informed.
We 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. We 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. While 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.”