Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis

Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis
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用于加速破坏性降解测试数据分析的半参数模型

DOI:
10.1080/00401706.2017.1321584
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发表时间:
2017
期刊:
影响因子:
2.5
通讯作者:
Yang, Qingyu
Yang, Qingyu
中科院分区:
工程技术3区
文献类型:
--
作者:
Xie, Yimeng;King, Caleb B.;Hong, Yili;Yang, Qingyu

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加速破坏性降解测试 (ADDT) 在工业中广泛用于评估材料的长期性能。尽管非参数方法已经有大量的统计研究,但当前的工业实践仍然是使用特定于应用的参数模型来描述 ADDT 数据。使用非参数方法的挑战来自于需要保留退化机制的物理意义,并在使用条件下进行预测的外推。受这一挑战的推动,我们提出了一种半参数模型来描述 ADDT 数据。我们使用单调 B 样条对退化路径进行建模,这不仅提供了假设很少的灵活模型,而且保留了退化机制的物理意义(例如,退化路径是单调的)。参数模型(例如阿伦尼乌斯模型)用于对退化和加速变量之间的关系进行建模,从而可以外推到使用条件。我们开发了一种有效的程序来估计模型参数。我们还使用模拟来验证所开发的程序,并证明半参数模型在模型错误指定下的稳健性。最后,通过多个工业应用来说明所提出的方法。本文有在线补充材料。
Accelerated destructive degradation tests (ADDT) are widely used in industry to evaluate materials’ long-term properties. Even though there has been tremendous statistical research in nonparametric methods, the current industrial practice is still to use application-specific parametric models to describe ADDT data. The challenge of using a nonparametric approach comes from the need to retain the physical meaning of degradation mechanisms and also perform extrapolation for predictions at the use condition. Motivated by this challenge, we propose a semiparametric model to describe ADDT data. We use monotonic B-splines to model the degradation path, which not only provides flexible models with few assumptions, but also retains the physical meaning of degradation mechanisms (e.g., the degradation path is monotonic). Parametric models, such as the Arrhenius model, are used for modeling the relationship between the degradation and the accelerating variable, allowing for extrapolation to the use condition. We develop an efficient procedure to estimate model parameters. We also use simulations to validate the developed procedures and demonstrate the robustness of the semiparametric model under model misspecification. Finally, the proposed method is illustrated by multiple industrial applications. This article has online supplementary materials.
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