Bayesian analysis of designed reliability improvement experiments with application to adaptive termination

In reliability improvement experiments, engineers are very interested in which factors affect the reliability of products. Furthermore, they hope to terminate experiment early without affecting the identified results. In this article, we assume the shape and scale parameters of Weibull distributions vary with experimental factors, and construct a Bayesian framework to identify important factors. We model the relationships between the parameters of Weibull distributions and experimental factors, and determine important factors based on Bayesian credibility intervals. Because the proposed method can analyze lifetime data with heavy censoring, we adaptive terminate experiment through estimating and comparing model coefficients under different experiment times. The results show that experiment time is shortened while the important factors are not changed.