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首页> 外文期刊>Reliability Engineering & System Safety >Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula
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Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula

机译：使用随机效应普通维纳工艺和D-VINE Copula分析多变量依赖加速降解数据

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摘要

A modern product usually shows multiple performance characteristics that degrade simultaneously. It is quite common that these degradation processes are dependent due to some common factors such as internal structure, operating conditions, and working history. Technically, the essence of modeling such dependent degradation processes is to find appropriate marginal degradation models and capture the dependence structure among the multiple performance characteristics. This paper develops a multivariate dependent accelerated degradation test model based on a random-effect general Wiener process and D-vine copula. The proposed model and statistical inference method analyze nonlinear accelerated degradation data considering three sources of uncertainty and construct the marginal distributions. To overcome the lack of flexibility and parameter restrictions of standard multivariate copulas, the pair-copula constructions and their vine graphical representations are applied to reveal and fully understand the complex and hidden dependence patterns in the multivariate degradation data. A simulation example and a real application on the accelerated degradation data of a tuner are provided to illustrate the performance and benefits of the proposed model and statistical estimation method.
机译：现代产品通常显示同时降解的多种性能特征。由于内部结构，操作条件和工作历史等一些常见因素，这些劣化过程依赖于这些劣化过程是依赖的。从技术上讲，建模这种受所劣化过程的本质是找到适当的边缘劣化模型，并捕获多种性能特征之间的依赖结构。本文基于随机效应普通维纳工艺和D-VINE Copula开发了多变量依赖性降解测试模型。考虑三个不确定性和构建边际分布，提出的模型和统计推理方法分析了非线性加速降解数据并构建边缘分布。为了克服标准多变量Copulas的缺乏灵活性和参数限制，对成对谱结构及其藤图形表示应用于揭示并完全理解多元化降级数据中的复杂和隐藏依赖模式。提供了一种关于调谐器的加速劣化数据的模拟示例和实际应用，以说明所提出的模型和统计估计方法的性能和益处。

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