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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability >Bayesian reliability applications of a combined lifecycle failure distribution
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Bayesian reliability applications of a combined lifecycle failure distribution

机译：组合生命周期故障分布的贝叶斯可靠性应用

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This research seeks to better understand how to update sectional time-to-failure (TTF) distributions, such as the Sandia National Laboratories' developed CoMBined Lifecycle (CMBL) distribution, when new operational TTF data become available. With a bathtub-shaped hazard function, the CMBL probability density function provides an application-friendly method for characterizing a component's failure or lifecycle distribution. Its five parameters are chosen specifically to make it relatively easy to elicit the probability of failure distribution from subject matter experts and limited data. Once a characterization of the component?s lifecycle in terms of failure probability is established, a methodology for how to update that characterization based on the availability of new operational failure data is required. The updating process presented here uses a Bayesian changepoint methodology to return updated CMBL distribution parameters based on new operational TTF data modelled as a Poisson process. In this methodology, the changepoints are determined first, and when combined with the counts of the TTF data, provide enough information to estimate the remaining CMBL distribution parameters. The method developed in this effort for updating the CMBL distribution and other TTF distributions should prove valuable in optimizing large scale system-of-systems supply/repair chain models.
机译：这项研究旨在更好地了解如何在新的运行TTF数据可用时，更新断面故障时间（TTF）分布，例如Sandia国家实验室开发的CoMBined Lifecycle（CMBL）分布。借助浴缸形危险函数，CMBL概率密度函数提供了一种易于使用的方法来表征组件的故障或生命周期分布。特别选择了它的五个参数，以使从主题专家和有限的数据中得出故障分布的可能性变得相对容易。一旦根据故障概率确定了组件生命周期的特征，就需要一种用于根据新的操作故障数据的可用性来更新该特征的方法。此处介绍的更新过程使用贝叶斯变化点方法，基于建模为泊松过程的新的操作TTF数据返回更新的CMBL分布参数。在这种方法中，首先确定更改点，然后将其与TTF数据的计数结合起来，即可提供足够的信息来估计剩余的CMBL分布参数。在此努力中开发的用于更新CMBL分布和其他TTF分布的方法，在优化大规模系统间供应/维修链模型中应该被证明是有价值的。

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