报告内容摘要：

New system reliability models and analysis tools are being developed to aid in the successful development and commercialization of certain novel and evolving technologies. System reliability analyses, involving multiple failure processes, are important and challenging research topics, particularly when failure processes, such as degradation processes and random shocks, are competing and dependent. When component degradation models are extended to complex systems with multiple components, different perspectives of dependency should be considered in system reliability modeling. In this research, potential dependence patterns are investigated among multiple failure processes within and among components in systems and probabilistic models are developed to assess system reliability performance. For the reliability modeling of complex systems, if one component in the system degrades or fails prematurely, it is possible that other components will also degrade or fail prematurely given the shared working environment, which means component failure times are dependent. Existing system reliability models are extended to perform quantitative analyses for system reliability considering that the damages to the two failure processes caused by shocks are dependent. The research is organized into several scenarios, i.e., dependent failure processes are considered in different ways. Stochastically dependent component degradation processes are also studied, and extended gamma process models are used to model the dependent degradation process. Based on these new reliability models, different maintenance policies are derived to provide cost effective maintenance.

报告人简介：

David W. Coit is a Professor in the Department of Industrial & Systems Engineering at Rutgers University, Piscataway, NJ, USA. His current teaching and research involves system reliability modeling and optimization, and energy systems optimization. His research has been funded by National Science Foundation (NSF), U.S. Army, U.S. Navy, industry, and power utilities. He has over 80 published journal papers and over 90 peer-reviewed conference papers. In 1999, he was awarded a CAREER grant from NSF to develop new reliability optimization algorithms considering uncertainty. In 2010, he was awarded a NSF grant to study the integration of quality and reliability models for evolving technologies. He received a BS degree in mechanical engineering from Cornell University, an MBA from Rensselaer Polytechnic Institute, and MS and PhD in industrial engineering from the University of Pittsburgh. He also has over ten years of experience working for IIT Research Institute (IITRI), Rome NY, where he was a reliability analyst, project manager, and engineering group manager He is a Department Editor for IIE Transactions and an Associate Editor for Journal of Risk and Reliability, and he is a member of IIE and INFORMS.
 
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