Title : Engineering Design under Uncertainty and Health Prognostics<?xml:namespace prefix = "o" ns = "urn:schemas-microsoft-com:office:office" />

Authors : Hu Chao., Youn Byeng D., Wang Pingfeng

 About the Authors :

 Chao Hu, Ph.D. received his B.E. degree in Engineering Physics from Tsinghua University in Beijing, China in 2007 and the Ph.D. degree in Mechanical Engineering at the University of Maryland, College Park in Maryland in 2011. He worked as a Principal Scientist at Medtronic, Inc. in Minnesota from 2011 to 2015. He is currently an Assistant Professor in the Department of Mechanical Engineering at the Iowa State University. His research interests are engineering design under uncertainty, design of lithium-ion energy storage systems, and prognostics and health management (PHM). Dr. Hu has received several awards and recognitions for his research, including: the Highly Cited Research Paper 2012-2013 in the Journal of Applied Energy in 2015; the Star of Excellence Individual Award at Medtronic in 2014; the Best Paper Award in the ASME Design Automation Conferences (DAC) in 2013; and the Best Paper Award in the IEEE PHM Conference in 2012. His research work has led to more than 60 peer-reviewed publications in the above areas.

Byeng D. Youn, Ph.D. received his Ph.D. degree from the department of Mechanical Engineering at the University of Iowa, Iowa City, IA, in 2001. He was a research associate at the University of Iowa (until 2004), an assistant professor in Michigan Technical University (until 2007), and an assistant professor in the University of Maryland College Park (until 2010). Currently, he is a professor at the School of Mechanical and Aerospace Engineering at Seoul National University, Republic of Korea. His research is dedicated to well-balanced experimental and simulation studies of system analysis and design, and he is currently exploring three research avenues: system risk-based design, prognostics and health management (PHM), and energy harvester design. Dr. Youn’s research and educational dedication has led to: many notable awards, including the ISSMO/Springer Prize for the Best Young Scientist in 2005 from the International Society of Structural and Multidisciplinary Optimization (ISSMO), and more than 100 publications in the area of system-risk-based design, PHM and energy harvester design.

Pingfeng Wang, Ph.D. received the B.E. degree in mechanical engineering from The University of Science and Technology, Beijing, China, in 2001, the M.S. degree in applied mathematics from Tsinghua University, Beijing, China, in 2006, and the Ph.D. degree in mechanical engineering from the University of Maryland, College Park, MD, USA, in 2010. He is currently an Associate Professor in the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. His research interests include engineering system design for reliability, failure resilience and sustainability, and prognostics and health management. Dr. Wang’s research has garnished him notable international awards including two times ASME Best Paper Awards in 2008 and 2013, respectively, 2012 IEEE PHM Best Paper Award, the National Science Foundation CAREER Award in 2014, the Young Researcher Award from the International Society of Green Manufacturing and Applications in 2012, and the Design Automation Young Investigator Award from the ASME in 2016. His dedicated research efforts have resulted in more than 100 publications in refereed journals and conference proceedings.

About the Book :

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l  Presents a comprehensive, up-to-date description of probabilistic analysis and design methods

l  Conveys a practical understanding of how uncertainties in system inputs can be efficiently and accurately propagated to uncertainties in system responses for reliability analysis and design

l  Contains hundreds of graphics illustrating technical concepts, procedures, and analysis of results

Introduction :

This book presents state-of-the-art probabilistic methods for the reliability analysis and design of engineering products and processes. It seeks to facilitate practical application of probabilistic analysis and design by providing an authoritative, in-depth, and practical description of what probabilistic analysis and design is and how it can be implemented. The text is packed with many practical engineering examples (e.g., electric power transmission systems, aircraft power generating systems, and mechanical transmission systems) and exercise problems. It is an up-to-date, fully illustrated reference suitable for both undergraduate and graduate engineering students, researchers, and professional engineers who are interested in exploring the fundamentals, implementation, and applications of probabilistic analysis and design methods.