Accelerated Life Tests of a Series System With Masked Interval Data Under Exponential Lifetime Distributions
Accelerated Life Tests of a Series System With Masked Interval Data Under Exponential Lifetime Distributions
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DOI：
10.1109/tr.2012.2209259
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发表时间：
2010-06
影响因子：
5.9
通讯作者：
T. Fan;Tsung-Ming Hsu
中科院分区：
文献类型：
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作者：
T. Fan;Tsung-Ming Hsu
本文讨论了在加速寿命试验下，当元件服从统计独立的指数寿命分布时，观察到区间数据的串联系统的可靠性分析。在串联系统中，如果任何一个组件发生故障，系统就会发生故障。通常包括屏蔽数据，其中未观察到导致系统故障的组件。首先，我们通过期望最大化算法应用最大似然方法，并使用参数自助法进行标准误估计。当掩蔽数据的比例高时，最大似然法由于缺乏信息而失败。在这种情况下，贝叶斯方法是一种适当的替代方法。因此，我们还研究了贝叶斯方法与主观先验分布的帮助下，马尔可夫链蒙特卡罗方法。我们得出的模型参数的统计推断，以及平均寿命，和系统和组件的可靠性函数。所提出的方法是通过一个数值例子模拟的基础模型下的各种掩蔽水平。
We will discuss the reliability analysis of a series system under accelerated life tests when interval data are observed, while the components are assumed to have statistically independent exponential lifetime distributions. In a series system, the system fails if any of the components fails. It is common to include masked data in which the component that causes failure of the system is not observed. First, we apply the maximum likelihood approach via the expectation-maximization algorithm, and use the parametric bootstrap method for the standard error estimation. When the proportion of the masking data is high, the maximum likelihood approach fails due to lack of information. A Bayesian approach is an appropriate alternative in such a case. Hence, we also study the Bayesian approach incorporated with a subjective prior distribution with the aid of the Markov chain Monte Carlo method. We derive statistical inference on the model parameters, as well as the mean lifetimes, and the reliability functions of the system and components. The proposed method is illustrated through a numerical example simulated from the underlying model under various masking levels.