Abstract Accelerated repeated measures degradation tests are often used to assess product or component reliability when there would be few or even no failures during a traditional life test. Such tests are used to estimate the failure-time distributions of highly reliable items in applications where it is possible to take repeated measures of some appropriate degradation measure. When engineers have valid prior information about failure mechanisms, it is important that such information be used in inference and test planning. Bayesian methods provide a vehicle for doing this. This article describes methods for selecting a Bayesian repeated measures accelerated degradation test plan when the degradation and acceleration model is linear in the parameters. A Bayesian criterion based on estimation precision of the failure-time quantile at use conditions is selected for finding optimum test plans. We use a large-sample approximation for the posterior distribution to simplify the planning criterion. The general equivalence theorem is used to check for global optimality of the optimum test plan. We also discuss how to find a compromise test plan that satisfies practical constraints while still providing good statistical properties.

中文翻译：

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用于规划加速重复测量退化测试的贝叶斯方法

摘要 加速重复测量退化试验常用于在传统寿命试验中几乎没有故障甚至没有故障时评估产品或部件的可靠性。此类测试用于估计应用中高度可靠的项目的故障时间分布，在这些应用中，可以重复测量某些适当的降级措施。当工程师有关于故障机制的有效先验信息时，将这些信息用于推理和测试计划就很重要。贝叶斯方法为此提供了一种工具。本文描述了当退化和加速模型在参数中是线性时选择贝叶斯重复测量加速退化测试计划的方法。选择基于使用条件下故障时间分位数估计精度的贝叶斯准则来寻找最佳测试计划。我们对后验分布使用大样本近似来简化规划标准。一般等价定理用于检查最优测试计划的全局最优性。我们还讨论了如何找到一个既满足实际约束又提供良好统计特性的折衷测试计划。