An optimal designed degradation experiment for reliability improvement

In conducting a design-of-experiments, with degradation data, to improve the reliability of highly reliable products, one of the most important topics for manufacturers is to identify important factors that affect the product reliability. Several decision variables such as inspection frequency, sample size, and termination time at each run, do influence the precision of identifying the important factors and the experimental cost. This paper proposes a systematic approach to the identification problem with respect to fractional factorial degradation experiments. First, an intuitively appealing identification rule is proposed. Next, the optimum test plan is derived using the criterion of minimizing the total experimental cost. The inspection frequency, sample size, and termination time at each run, that are needed by this identification rule are computed by solving a nonlinear integer programming model with a minimum probability of correct selection. A simulation study investigates the efficiency of this method.