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首页> 外文会议>International Conference on Reliability Systems Engineering >Bivariate Degradation Modeling And Reliability Evaluation Of accelerometer Based On Physics-Statistics Model And Copula Function
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Bivariate Degradation Modeling And Reliability Evaluation Of accelerometer Based On Physics-Statistics Model And Copula Function

机译：基于物理统计模型和Copula功能的加速度计的双变量降解建模与可靠性评价

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The failure mechanism of modern product has been becoming more complicated. Many highly reliable products usually have complex structure and many functions. They may have two or more performance characteristics. All the performance characteristics can reflect the product's performance degradation over time. What's more, the degradations of performance characteristics may be dependent. Therefore, it is an urgent problem how to describe the multiple performance degradation, establish reliability model of products, and eventually propose a corresponding reliability evaluation method. Bivariate degradation is a special form of multivariate degradation. But it's the most important and fundamental situation. Bivariate degradation analysis is the basis of multiple degradation. To solve this issue, this paper we take a certain type of accelerometer as the research object, selecting two typical performance parameters: scale factor and bias factor and using the accelerated degradation test data for reliability analysis. Here, we build physical statistical models based on physical model and Brownian motion to describe the degradation paths of these two performance characteristics separately. The dependence of the performance characteristics can be described by copula function. After these work, we give out the joint distribution function of the performance characteristics. In order to estimate the product's reliability as accurate as possible, the parameters of the joint distribution function are estimated as a whole. As the model is very complicated and analytically intractable, we use the Bayesian Markov chain Monte Carlo method to simulate the maximum likelihood estimates, which allows the maximum-likelihood estimates of the parameters to be determined in an efficient manner. Finally the reliability function is deduced through the joint distribution of degradations and reliability function of the each performances characteristics. At the end of paper, the effectiveness of the proposed method is verified through accelerometer accelerated stress test.
机译：现代产品的失败机制一直变得越来越复杂。许多高度可靠的产品通常具有复杂的结构和许多功能。它们可能有两个或多个性能特征。所有性能特征都可以反映产品随着时间的推移的性能下降。更重要的是，性能特征的降级可能是依赖的。因此，这是一种迫切的问题如何描述多种性能下降，建立产品可靠性模型，并最终提出了一种相应的可靠性评估方法。双变量降解是一种特殊形式的多变量降解。但这是最重要和最重要的情况。双变化降解分析是多重降解的基础。为了解决这个问题，本文我们采取了某种类型的加速度计作为研究对象，选择两个典型的性能参数：比例因子和偏置因子，并使用加速的降级测试数据进行可靠性分析。在这里，我们基于物理模型和布朗运动构建物理统计模型，分别描述这两个性能特征的劣化路径。性能特征的依赖可以通过Copula功能来描述。在这些工作之后，我们透露了性能特征的联合分布函数。为了尽可能准确地估算产品的可靠性，估计联合分布函数的参数估计。随着模型非常复杂和分析难以相容，我们使用贝叶斯马尔可夫链蒙特卡罗方法来模拟最大似然估计，这允许以有效的方式确定参数的最大似然估计。最后，通过每个性能特性的降解和可靠性函数的联合分布推导到可靠性函数。在纸结束时，通过加速度计加速应力测试来验证所提出的方法的有效性。

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