Accelerated Degradation Test Planning Using the Inverse Gaussian Process
Accelerated Degradation Test Planning Using the Inverse Gaussian Process
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DOI：
10.1109/tr.2014.2315773
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发表时间：
2014-04
影响因子：
5.9
通讯作者：
Z. Ye;Liangpeng Chen;L. Tang;M. Xie
中科院分区：
文献类型：
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作者：
Z. Ye;Liangpeng Chen;L. Tang;M. Xie
逆高斯（IG）过程模型已被证明是退化分析中的一个重要类别。在本文中，当潜在退化遵循逆高斯过程时，我们对最优恒定应力加速退化试验（ADT）规划感兴趣。我们首先考虑无随机效应的逆高斯过程的加速退化试验规划。推导出了下分位数估计的渐近方差，并且规划的目标是通过适当选择测试应力以及分配给每个应力的样本数量来使该方差最小化。接下来，考虑随机效应逆高斯过程模型的加速退化试验规划。然后我们应用逆高斯过程来拟合一个部件的应力松弛数据，并使用所开发的方法来辅助最优加速退化试验设计。
The IG process models have been shown to be an important family in degradation analysis. In this paper, we are interested in optimal constant-stress accelerated degradation tests (ADTs) planning when the underlying degradation follows the inverse Gaussian (IG) process. We first consider ADT planning for the IG process without random effects. Asymptotic variance of the estimate of a lower quantile is derived, and the objective of the planning is to minimize this variance by properly choosing the testing stresses, and the number of samples allocated to each stress. Next, ADT planning for a random-effects IG process model is considered. We then applied the IG process to fit the stress relaxation data of a component, and use the developed methods to help with the optimal ADT design.