Multi-state reliability demonstration tests

ABSTRACT

Reliability demonstration tests have important applications in reliability assurance activities to demonstrate product quality over time and safeguard companies’ market positions and competitiveness. With greatly increasing global market competition, conventional binomial reliability demonstration tests based on binary test outcomes (success or failure) at a single time point become insufficient for meeting consumers’ diverse requirements. This article proposes multi-state reliability demonstration tests (MSRDTs) for demonstrating reliability at multiple time periods or involving multiple failure modes. The design strategy for MSRDTs employs a Bayesian approach to allow incorporation of prior knowledge, which has the potential to reduce the test sample size. Simultaneous demonstration of multiple objectives can be achieved and critical requirements specified to avoid early/critical failures can be explicitly demonstrated to ensure high customer satisfaction. Two case studies are explored to demonstrate the proposed test plans for different objectives.

KEYWORDS:

• binomial demonstration test
• consumer's risk
• minimum sample size
• Monte Carlo integration

About the authors

Suiyao Chen is a Ph.D. student in the Department of Industrial and Management Systems Engineering at University of South Florida. He received his B.S. degree (2014) in Economics from Huazhong University of Science and Technology and M.A. degree (2016) in Statistics from Columbia University. His research focus is on statistical reliability data analysis, demonstration tests design, and data analytics.

Lu Lu is an Assistant Professor of Statistics in the Department of Mathematics and Statistics at the University of South Florida in Tampa. She was a postdoctoral research associated in the Statistics Sciences Group at Los Alamos National Laboratory. She earned a doctorate in statistics from Iowa State University in Ames, IA. Her research interests include reliability analysis, design of experiments, response surface methodology, survey sampling, and multiple objective/response optimization.

Mingyang Li is an assistant Professor in the Department of Industrial & Management Systems Engineering at the University of South Florida. He received his Ph.D. in systems & industrial engineering and his M.S. in statistics from the University of Arizona in 2015 and 2013, respectively. He also received his M.S. in mechanical & industrial engineering from the University of Iowa in 2010 and his B.S. in control science & engineering from Huazhong University of Science and Technology in 2008. His research interests include reliability and quality assurance, Bayesian data analytics and system informatics. Dr. Li is a member of INFORMS, IISE, and ASQ.

Funding

This work was supported in part by National Science Foundation under Grant BCS-1638301 and in part by University of South Florida Research & Innovation Internal Awards Program under Grant No. 0114783.