Designing An Accelerated Degradation Experiment By Optimizing The Interval Estimation Of The Mean-Time-To-Failure

[[abstract]]Reliability is an important attribute of a product's quality. Hence, assessing a product's reliability information is an essential task of improving continously the product's quality. Accelerated degradation tests (ADTs) are widely used to assess the reliability information for very-highly reliable products whose quality characteristics degrade over time very slowly. In estimating a product's reliability information, the interval estimation is more favorite to the manufacturer than the point estimation. Several decision variables, such as the inspection frequency, the sample size, and the termination time at each stress level, are closely related to the precision of the interval estimation and the experimental cost in an ADT. Clearly, an inappropriate setting of these decision variables wastes the experimental resources as well as reduces the precision of data analysis. The purpose of this paper is to deal with the problem of designing an ADT such that the interval estimation of the mean-time-to-failure (MTTF) at use condition is efficient. More specifically, with respect to the products whose degradation rates follow a lognormal distribution, under the constraint that the total experimental cost does not exceed a pre—determined budget, a mixed nonlinear programming is built to determine the optimal combinations of these decision variables at each stress level and the optimal combination of the CIs of the parameters involved in the MTTF's expression such that the expected width of a 100(1- γ)% CI of the MTTF is minimal