Utilizing experimental degradation data for warranty cost optimization under imperfect repair

Manufacturers usually want to predict the warranty cost for new products under affordable maintenance policies. With insufficient reliability information, experimental degradation tests are commonly conducted to predict the field reliability before the products are put on the market. In this paper, we propose a novel warranty cost optimization framework based on degradation data within a finite warranty period under the assumption of imperfect repairs. The expected number of warranty claims is given in the analytical form. Two sources of uncertainty are considered to estimate the field reliability for more realistic warranty cost prediction: the uncertainty in experimental data and the variation in field conditions. Effects of imperfect repairs are assumed to be random. The warranty cost for a single repair is assumed to be associated with the improvement factor of imperfect repairs. Optimal imperfect repair policy is obtained by minimizing the expected warranty cost for each sold product. Further, the proposed framework can facilitate the interval prediction for warranty cost. Numerical results show that the proposed analytical method to evaluate warranty claims significantly outperforms simulation methods from the perspective of computational efforts. Finally, an application example of degradation tests along with sensitivity analysis is presented to illustrate the proposed framework.