@article{41f5e16c6e144ba1a6d15294b579601b,
title = "Accelerated burn-in and condition-based maintenance for n -subpopulations subject to stochastic degradation",
abstract = "For some engineering design and manufacturing applications, particularly for evolving and new technologies, populations of manufactured components can be heterogeneous and consist of several subpopulations. The co-existence of n subpopulations can be common in devices when the manufacturing process is still maturing or highly variable. A new model is developed and demonstrated to determine accelerated burn-in and condition-based maintenance policies for populations composed of distinct subpopulations subject to stochastic degradation. Accelerated burn-in procedures with multiple accelerating factors are considered for the degradation-based heterogeneous populations. Condition-based maintenance is implemented during field operation after burn-in procedures. The proposed joint accelerated burn-in and condition-based maintenance policy are compared with two benchmark policies: a joint accelerated burn-in and age-based preventive replacement policy and a condition-based maintenance-only policy. Numerical examples are provided to illustrate the proposed procedure. Sensitivity analysis is performed to investigate the value of joint accelerated burn-in and condition-based maintenance policy and to indicate which type of policy should be applied according to different conditions and device characteristics. {\textcopyright} 2014",
keywords = "accelerated burn-in, condition-based maintenance, mixture degradation model, n-Subpopulations, stochastic degradation",
author = "Yisha Xiang and Coit, {David W.} and Feng, {Qianmei May}",
note = "Funding Information: The work of the first author was supported by the National Natural Science Foundation of China under grant 71301171 and by the Chinese Ministry of Education under grant 11YJC630228. The work of the second and third authors was supported by National Science Foundation under grants CMMI-0970140 and CMMI-0969423. Funding Information: David W. Coit is a Professor in the Department of Industrial & Systems Engineering at Rutgers University. His current research and teaching interests involve reliability modeling and optimization, risk analysis, and multi-objective optimization considering uncertainty. He received a B.S. degree in Mechanical Engineering from Cornell University, an MBA from Rensselaer Polytechnic Institute, and M.S. and Ph.D. degrees in Industrial Engineering from the University of Pittsburgh. He also has over 10 years of experience working for IIT Research Institute (IITRI), Rome, NY (now called Alion Science and Technology), where he was a reliability analyst, project manager, and engineering group manager. In 1999, he was awarded a CAREER grant from the National Science Foundation (NSF) to study reliability optimization. His research has been funded by NSF, U.S. Navy, U.S. Army, power utilities, and industry. He is a member of IIE and INFORMS. Funding Information: Qianmei Feng is an Assistant Professor and the Brij and Sunita Agrawal Faculty Fellow in the Department of Industrial Engineering at the University of Houston, Houston, Texas. She received a Ph.D. degree in Industrial Engineering from the University of Washington, Seattle, Washington, in 2005. Her research interests include the areas of system modeling, analysis and optimization in quality and reliability engineering, with applications in evolving technologies (e.g., MEMS, biomedical implant devices), homeland security, and healthcare. She has published over 20 articles in refereed journals, such as IIE Transactions, IEEE Transactions on Reliability, Reliability Engineering and System Safety, Computers and Industrial Engineering, Journal of Operational Research Society, and Risk Analysis. Her research has been supported by the National Science Foundation, Department of Homeland Security, Texas Department of Transportation, and the Texas Higher Education Coordinating Board. She is a member of INFORMS, IIE, ASQ, and Alpha Pi Mu.",
year = "2014",
month = oct,
day = "3",
doi = "10.1080/0740817X.2014.889335",
language = "English",
volume = "46",
pages = "1093--1106",
journal = "IIE Transactions (Institute of Industrial Engineers)",
issn = "0740-817X",
number = "10",
}