On reliability analysis of one-shot devices with manufacturing defects

Abstract

One-shot device test data have attracted increased attention. The working condition of a one-shot device is unknown until testing the device. In this paper, we consider one-shot device test data with defects that are induced in a realistic manufacturing process. The maximum likelihood approach is proposed for estimating the mean-time-to-failure. In this study, masked data are also considered when we cannot distinguish whether a failed device is originally defective or not. A Monte Carlo simulation study is conducted to evaluate the impacts of the masking effect on the estimation under different settings. Some practical guidelines and recommendations are provided.

Keywords:

• defects
• gamma distribution
• masking
• maximum likelihood estimators
• one-shot device
• Weibull distribution

Acknowledgment

Our sincere thanks go to the reviewers for their useful comments and suggestions, which have resulted in this improved version.

Additional information

Funding

H.K.T. Ng’s work was supported by a grant from the Simons Foundation (#709773 to Tony Ng). This work of the third author was supported by the Dean’s Research Fund of the Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Hong Kong Special Administrative Region, China (Project No. FLASS/DRF 04618) and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. [T32-101/15-R]).

Notes on contributors

Xiangwen Shang

Xiangwen Shang is an adjunct lecturer in the Department of Statistical Science at Southern Methodist University. He received a Bachelor of Science degree in Applied Mathematics from Tongji University (Shanghai, China) in 2010, a Master's degree, and a Ph.D. degree in Statistical Science from the Southern Methodist University in 2014 and 2021, respectively. His research interests include reliability analysis, statistical inference, and analysis of censored data.

Hon Keung Tony Ng

Hon Keung Tony Ng is a professor in the Department of Mathematical Sciences at Bentley University. He received a Ph.D. degree in Mathematics from McMaster University (Hamilton, Canada) in 2002. His research interests include reliability, censoring methodology, ordered data analysis, nonparametric methods, and statistical inference. He is an associate editor of Communications in Statistics, Computational Statistics, IEEE Transactions on Reliability, Journal of Statistical Computation and Simulation, Naval Research Logistics, Sequential Analysis, and Statistics & Probability Letters. He is a fellow of the American Statistical Association, an elected senior member of IEEE, and an elected member of the International Statistical Institute.

Man Ho Ling

Man Ho Ling is an associate professor in the Department of Mathematics and Information Technology at the Education University of Hong Kong. He received his B.Sc. and M.Phil. degrees from Hong Kong Baptist University, Hong Kong, in 2005 and 2008, respectively. He finished his Ph.D. from McMaster University, Hamilton, ON, Canada, in 2012. His research interests include accelerated life testing analysis, degradation data analysis, reliability and survival analyses, statistical inference under censoring, and statistical computing.