iSYS R&D(Intelligent SYStem Reliability & Design Laboratory)
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Research Risk Analysis & Design Prognosis & Health Management(PHM) Energy Harvester Design Verification & Validation	Motivation: To support critical decision-making processes such as maintenance replacement and product design, engineering systems composed of multiple components, complex joints, and various materials, such as distributed manufacturing facilities, electronic devices, advanced military systems, require constant sensory health monitoring and Residual Useful Life (RUL) prediction. Research on real-time diagnosis and prognosis which interprets data acquired by smart sensors and distributed sensor networks, and utilizes these data streams in making critical decisions provides significant advancements across a wide range of application. Prognostics and Health Management (PHM) is one area that is positioned to significantly benefit in this regard due to the pervasive nature of design and maintenance activities throughout the manufacturing and service sectors. Figure below shows a rough PHM picture schematically. Prognostics and Health Management (PHM) Research Highlight: • This research focuses on the development of a generally applicable prognostic framework and the integration of this framework into engineering system design for resilience. In this research Bayesian methods, for example, non-conjugate Bayesian updating, Relevance Vector Machine (RVM), and Sequential Monte Carlo (SMC) -Based Filtering techniques, will be intensively used for the real-time system RUL prediction and continuous updating. Specifically, to successfully accomplish this research, the following research thrusts are identified: • 01: The Development of A Generic Prognostics Framework • 02: The Development of Effective PHM Algorithms (Model-Based & Data-Driven) • 03: The Effectiveness Evaluation of PHM Systems • 04: PHM Integrated Resilient System Design. Relevant Publications: 1. Youn B. D. and Wang P., “A Generic Bayesian Framework for Decision-Centered Life and Reliability Prognostics,” IEEE， Prognostics and Health Management, 2008. 2. Wang P. and Youn B. D., “A Data-Driven Structural Health Prognostics Framework Using Relevance Vector Machine” IEEE Transactions on Reliability, Submitted, 2009.