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首页> 外文会议>2013 Proceedings - Annual Reliability and Maintainability Symposium >A Bayesian approach to online system health monitoring
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A Bayesian approach to online system health monitoring

机译：在线系统健康监控的贝叶斯方法

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This paper introduces a new online system health monitoring methodology utilizing Bayesian Belief Networks. The developed methodology enables inference with limited number of monitoring points optimally placed to obtain information on functional states of components, subsystems, and relevant physical parameters affecting the reliability of elements of the system. The approach integrates physics of failure modes when available with traditional reliability data (e.g., failures and demands) and is (1) capable of assessing current state of a system's health and probabilistic assessment of the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have caused or are likely to result in system failure or degradation (diagnosis). Continuous health assessment is made possible through the application of dynamic BBNs. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (“upward” inference); how to infer the health of a subsystem or component based on knowledge of the health of the main system (“downward” inference); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (“distributed” inference). The methodology and algorithms are demonstrated through an example.
机译：本文介绍了一种利用贝叶斯信念网络的新的在线系统健康监控方法。所开发的方法论使得能够推理出有限数量的监视点，这些监视点被最佳地放置以获取有关组件，子系统的功能状态以及影响系统元素可靠性的相关物理参数的信息。该方法将故障模式的物理原理与传统的可靠性数据（例如，故障和需求）结合在一起，并且能够（1）能够评估系统健康状况的当前状态以及对系统剩余寿命的概率评估（预后），并且（ 2）通过适当的数据处理和解释，可以指向导致或可能导致系统故障或降级（诊断）的系统元素。通过使用动态BBN，可以进行持续的健康评估。所提出的方法旨在回答一些重要问题，例如如何基于某些子系统上有限数量的监视点来推断系统的运行状况（“向上”推断）；如何基于对主系统运行状况的了解来推断子系统或组件的运行状况（“向下”推断）；以及如何基于其他子系统的健康状况来推断子系统的健康状况（“分布式”推断）。通过示例演示了该方法和算法。

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