Title: Reliability Modeling and Optimization for Systems of Degrading Components

Time: 13:30 to 15:00, Oct. 22, 2018

Place: F210, School of Mechanical Engineering

Host: PAN Ershun, Professor (Department of Industrial Engineering and Management)

Short Bio:

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 110 published journal papers and over 90 peer-reviewed conference papers. He has been awarded several NSF grants, including a CAREER grant from NSF to develop new reliability optimization algorithms considering uncertainty. He was also the recipient of the P. K. McElroy award, Alain O. Plait award and Willian A. J. Golomski award for best papers and tutorials at the Reliability and Maintainability Symposium (RAMS). He also has over ten years of experience working for IIT Research Institute (IITRI), Rome NY. 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 is an Associate Editor for IISE Transactions and a Department Editor for Journal of Risk and Reliability, and he is a member of IIE and INFORMS.

Abstract:

New system reliability models and analysis tools have been developed for systems of degrading components. 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 are needed for 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 plans.