Abstract
This paper presents exact optimum test plans for simple time-step stress models in accelerated life testing. An exponential life distribution with a mean that is a log-linear function of stress, and a cumulative exposure model are assumed. Maximum likelihood methods are used to estimate the parameters of such models. Optimum test plans are obtained by minimizing the mean square error between the maximum likelihood estimate of a certain moment of the lifetime at a design stress and the real moment. The advantage of our optimum test plans is that it does not require large number of items to be tested. We also compare our results with test plans obtained by minimizing the asymptotic variance of the maximum likelihood estimate of the mean life at a design stress.
Keywords
Cumulative exposure model; exponential distribution; extrapolation; loss function; maximum likelihood
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Xiong, C.
(1998).
"OPTIMUM DESIGN ON STEP-STRESS LIFE TESTING,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1290
OPTIMUM DESIGN ON STEP-STRESS LIFE TESTING
This paper presents exact optimum test plans for simple time-step stress models in accelerated life testing. An exponential life distribution with a mean that is a log-linear function of stress, and a cumulative exposure model are assumed. Maximum likelihood methods are used to estimate the parameters of such models. Optimum test plans are obtained by minimizing the mean square error between the maximum likelihood estimate of a certain moment of the lifetime at a design stress and the real moment. The advantage of our optimum test plans is that it does not require large number of items to be tested. We also compare our results with test plans obtained by minimizing the asymptotic variance of the maximum likelihood estimate of the mean life at a design stress.
