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Utilizing experimental degradation data for warranty cost optimization under imperfect repair

机译：利用实验性降级数据在不完善的维修条件下优化保修成本

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摘要

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.
机译：制造商通常希望根据负担得起的维护政策来预测新产品的保修成本。在缺乏足够的可靠性信息的情况下，通常会进行实验性退化测试来预测产品投放市场之前的现场可靠性。在本文中，我们提出了一种新颖的保修成本优化框架，该框架基于不完善维修的假设下有限保修期内的退化数据。分析表格中给出了预期的保修索赔数量。考虑了两种不确定性来源来估计现场可靠性，以进行更现实的保修成本预测：实验数据的不确定性和现场条件的变化。不完善的维修效果被认为是随机的。假定单次维修的保修成本与不完善维修的改善因素有关。通过将每种售出产品的预期保修成本降至最低，可以获得最佳的不完善维修政策。此外，提出的框架可以促进保修成本的间隔预测。数值结果表明，从计算工作的角度来看，所提出的评估保修索赔的分析方法明显优于模拟方法。最后，给出了退化测试和灵敏度分析的应用示例，以说明所提出的框架。

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