Batch Variability in Accelerated-Degradation Testing
Batch Variability in Accelerated-Degradation Testing
复制标题
加速降解测试中的批次变异性
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
N. Doganaksoy
中科院分区：
文献类型：
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作者：
Ming Li;N. Doganaksoy
问题：本文的研究目的是开发一种用于高压应用的新型密封件。密封弱化不仅会对整体器械性能产生不利影响，而且还可能导致潜在的安全性问题。该密封件的设计可在应用环境中使用10年。开发团队进行了温度加速试验，以研究密封强度的降解。用于将工程模型拟合到数据的特设程序不容易估计强度分布的较低分位数，也不容易获得感兴趣数量的统计置信界限。为了指导工程和管理决策，作者对数据的统计分析和建模提出了建议。方法：在我们对数据的初步分析中，我们使用了基于密封强度和温度的工程关系的标准统计模型。对模型最大似然拟合残差的分析显示，数据中还有一个未考虑的变异来源。这一发现对标准统计模型得出的推论提出了质疑，因为该模型在代表数据方面存在不足。我们扩展了标准模型，通过非线性混合效应模型来适应这种新的变异性来源。结果：扩展模型被证明足以解释数据中的过度变异源。基于这一模型的推断显示出更大的不确定性，并强调需要确定和减轻差异的根本原因。一个狭窄的咨询问题导致了一个重大发现，为与海豹突击队进行有意义的接触铺平了道路。案例研究表明了充分理解实验过程的重要性以及尖锐残差图在统计分析和建模中的有用性。
Problem: The study that motivated this article concerns the development of a new seal to be used in a high-pressure application. A weakened seal would not only adversely affect the overall device performance but it could also lead to potential safety concerns. The seal was designed to perform for 10 years in the application environment. The development team had undertaken a temperature accelerated test to study the degradation of seal strength. The ad hoc procedure used to fit an engineering model to the data did not readily allow estimation of lower quantiles of the strength distribution and obtaining statistical confidence bounds on quantities of interest. The authors were approached for advice on statistical analysis and modeling of the data in order to guide engineering and management decision making. Approach: In our initial analysis of the data, we used a standard statistical model based on the engineering relationship for seal-strength and temperature. The analysis of residuals from the maximum-likelihood fit of the model revealed an additional source of variation in the data that had not been accounted for. This finding cast doubt on the inferences that resulted from the standard statistical model due to the inadequacy of the model in representing the data. We extended the standard model to accommodate this new source of variability through nonlinear mixed effects modeling. Results: The extended model was shown to adequately account for the excess source of variation in the data. The inferences based on this model showed greater uncertainty and highlighted the need to identify and mitigate the root cause of the variation. A narrow consulting question led to a significant discovery that paved the way for a meaningful engagement with the seal team. The case study shows the importance of fully understanding the experimental process and the usefulness of incisive residuals plots in statistical analysis and modeling.