基于双变量Wiener模型的阶跃应力模型的失效推断及Optimization
Failure Inference and Optimization for Step Stress Model Based on Bivariate Wiener Model
摘要：
在本文中，我们考虑的情况下，寿命试验，其中的测试单元的失效时间不确定性相关的一个可观测的随机时变协变量。在这种情况下，失效时间和标记值的联合分布将有助于模拟步进应力寿命试验。加速这样的实验的问题被认为是本文的主要目的。我们提出了一个步骤应力加速模型的基础上，一个组件作为潜在的（不可观测的）退化过程，确定故障时间和其他作为标记过程，其退化值记录在故障时间的双变量维纳过程。基于所提出的模型的参数推断进行了讨论，并提出了获得最佳时间为改变应力水平的优化过程。优化准则是使产品寿命分布的百分位数的极大似然估计的近似方差最小。
英文摘要：
In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products' lifetime distribution.
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