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Quantifying reliability uncertainty from catastrophic and margin defects: A proof of concept

机译:量化来自灾难性和边际缺陷的可靠性不确定性:概念验证

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We aim to analyze the effects of component level reliability data, including both catastrophic failures and margin failures, on system level reliability. While much work has been done to analyze margins and uncertainties at the component level, a gap exists in relating this component level analysis to the system level. We apply methodologies for aggregating uncertainty from component level data to quantify overall system uncertainty. We explore three approaches towards this goal, the classical Method of Moments (MOM), Bayesian, and Bootstrap methods. These three approaches are used to quantify the uncertainty in reliability for a system of mixed series and parallel components for which both pass/fail and continuous margin data are available. This paper provides proof of concept that uncertainty quantification methods can be constructed and applied to system reliability problems. In addition, application of these methods demonstrates that the results from the three fundamentally different approaches can be quite comparable.
机译:我们旨在分析组件级可靠性数据(包括灾难性故障和裕度故障)对系统级可靠性的影响。尽管已经做了很多工作来分析组件级别的裕度和不确定性,但是在将此组件级别分析与系统级别相关联方面仍然存在差距。我们采用方法来汇总组件级数据中的不确定性,以量化整体系统的不确定性。我们探索实现这一目标的三种方法,即经典的矩量法(MOM),贝叶斯方法和Bootstrap方法。这三种方法用于量化具有合格/不合格数据和连续裕量数据的混合串联和并联组件系统的可靠性不确定性。本文提供了可以构造不确定性量化方法并将其应用于系统可靠性问题的概念证明。此外,这些方法的应用表明,三种根本不同的方法所产生的结果是相当可比的。

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