Abstract
This article presents a novel accelerated reliability testing framework for mission-oriented systems. The system to be tested is assumed to suffer from cumulative degradation and traumatic shocks with increasing intensity. We propose a new optimality criterion that minimizes the asymptotic variance of the predicted reliability evaluated at the mission’s end time. Two usage scenarios are considered in this study: one is to assume that systems are brand new at the start of the mission and the other is that systems are randomly selected from used ones under pre-determined policies. Optimal test plans for both scenarios are obtained via delta methods by utilizing the Fisher information. The global optimality of test plans is verified using general equivalence theorems. A revisited example of a carbon-film resistor is presented to illustrate the efficiency and robustness of optimal test plans for both new and randomly aged systems. The result shows that the test plan tends to explore more on lower stress levels for randomly aged systems. Furthermore, we conduct simulation studies and explore compromise test plans for the example.
Acknowledgments
We are grateful to the editors and anonymous referees for their insightful comments that led to a substantial improvement to an earlier version of the paper.
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Notes on contributors
Xiujie Zhao
Xiujie Zhao is a postdoc fellow at City University of Hong Kong. He received a B.E. degree from Tsinghua University, an M.S. degree from the Pennsylvania State University and a Ph.D. degree from City University of Hong Kong, all in industrial engineering. His research interests include accelerated reliability testing, degradation modeling, maintenance optimization and design of experiments. His papers have appeared in IISE Transactions, Journal of Quality Technology, IEEE Transactions on Reliability, Reliability Engineering & System Safety, among others.
Kangzhe He
Kangzhe He is a Ph.D. student in the Department of Systems Engineering and Engineering Management at City University of Hong Kong. He received a B.S. degree in statistics from the University of Science and Technology of China in 2018. His research interests include stochastic modeling for complex systems, system maintenance and industrial statistics.
Way Kuo
Way Kuo is President of City University of Hong Kong. Before joining CityU, he was Dean of Engineering at the University of Tennessee, Knoxville. Previously, he was with Texas A&M University, Iowa State University, and Bell Labs. He is a member of the U.S. National Academy of Engineering and Academia Sinica in Taiwan and a foreign member of the Chinese Academy of Engineering. He is Fellow of the American Society for Quality, the Institute of Electrical and Electronics Engineers, the Institute for Operations Research and the Management Sciences, the American Statistical Association, and the Institute of Industrial and Systems Engineers.
Min Xie
Min Xie received his Ph.D. from Linkoping University, Sweden in 1987. He did his undergraduate study and received an MSc at the Royal Institute of Technology in Sweden in 1984. He joined the National University of Singapore in 1991 as one of the first recipients of the prestigious Lee Kuan Yew Research Fellowship, and he is currently a chair professor at the City University of Hong Kong. He has authored or co-authored numerous refereed journal papers and several books. He is a Department Editor of IISE Transactions and Editor of Reliability Engineering & System Safety and serves in a number of other international journals. He has organized many international conferences, and also 50 Ph.D. students have graduated under his supervision. He was elected fellow of IEEE in 2005.