A Robust Approach for Estimating Field Reliability Using Aggregate Failure Time Data
A Robust Approach for Estimating Field Reliability Using Aggregate Failure Time Data
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DOI：
10.1109/rams.2019.8769305
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Samira Karimi;H. Liao;E. Pohl
中科院分区：
文献类型：
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作者：
Samira Karimi;H. Liao;E. Pohl
现场系统的故障时间数据通常从系统的实际用户处获得。由于各种操作偏好和/或技术障碍，大部分现场数据被收集为汇总数据，而不是单个单元的确切故障时间。使用这些数据的挑战在于，与使用单个故障时间相比，所获得的信息更简洁但不太精确。在建模聚合故障时间数据的最重要的需求是选择一个适当的概率分布和统计推断程序能够处理数据聚合的发展。虽然一些概率分布，如伽玛分布和逆高斯分布，有众所周知的封闭形式的表达式的概率密度函数的聚集数据，使用这样的分布限制了现场可靠性估计的应用。对于可靠性从业者来说，使用一种鲁棒的方法来处理聚合故障时间数据而不限于少数概率分布是非常宝贵的。本文研究了相型（PH）分布作为一种候选分布模型的总体失效时间数据的应用。提出了一种期望最大化算法来获得模型参数的极大似然估计，并给出了可靠性估计的置信区间。仿真和数值研究表明，鲁棒的方法是相当强大的，因为PH分布在模仿各种概率分布的高能力。在可靠性工程领域，有有限的工作建模汇总数据的现场可靠性估计。在这项工作中描述的分析和统计推断方法提供了一个强大的工具，用于分析聚合故障时间数据的第一次。
Failure time data of fielded systems are usually obtained from the actual users of the systems. Due to various operational preferences and/or technical obstacles, a large proportion of field data are collected as aggregate data instead of the exact failure times of individual units. The challenge of using such data is that the obtained information is more concise but less precise in comparison to using individual failure times. The most significant needs in modeling aggregate failure time data are the selection of an appropriate probability distribution and the development of a statistical inference procedure capable of handling data aggregation. Although some probability distributions, such as the Gamma and Inverse Gaussian distributions, have well-known closed-form expressions for the probability density function for aggregate data, the use of such distributions limits the applications in field reliability estimation. For reliability practitioners, it would be invaluable to use a robust approach to handle aggregate failure time data without being limited to a small number of probability distributions. This paper studies the application of phase-type (PH) distribution as a candidate for modeling aggregate failure time data. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters, and the confidence interval for the reliability estimate is also obtained. The simulation and numerical studies show that the robust approach is quite powerful because of the high capability of PH distribution in mimicking a variety of probability distributions. In the area of reliability engineering, there is limited work on modeling aggregate data for field reliability estimation. The analytical and statistical inference methods described in this work provide a robust tool for analyzing aggregate failure time data for the first time.