Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model
Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model
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DOI：
10.1177/1748006x11428107
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发表时间：
2012-01
期刊：
影响因子：
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通讯作者：
K. Groth;A. Mosleh
中科院分区：
文献类型：
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作者：
K. Groth;A. Mosleh
在概率风险评估界，人们普遍认为有必要改进人的可靠性分析(HRA)的科学基础。这导致了一些独立研究的努力，以收集经验数据来验证人力资源评估方法，以及一些独立研究努力，以改进在人力资源评估中使用的人类绩效的理论模型。本文介绍了一种方法，将多个经验数据来源与经过验证的理论模型仔细地结合在一起，以加强定性和定量的HRA应用。该方法使用一套全面的绩效影响因素来组合来自不同来源的数据。此外，本文描述了如何使用数据来收集对绩效影响因素之间的关系的洞察，并构建定量的HRA因果模型。为了说明该方法是如何应用的，我们引入了贝叶斯网络模型，该模型是将该方法应用于两个来自核电站运营的人类绩效数据的来源。提出的模型被引入来演示如何从HRA数据中发展因果洞察力，以及如何将这些洞察力结合到量化的HRA模型中。本文中的方法论为谨慎地将新出现的人类绩效数据来源纳入改进的HRA方法提供了一条前进的道路。提出的模型是下一代数据知情的、经过理论验证的HRA方法的起点。
Within the probabilistic risk assessment community, there is a widely acknowledged need to improve the scientific basis of human reliability analysis (HRA). This has resulted in a number of independent research efforts to gather empirical data to validate HRA methods and a number of independent research efforts to improve theoretical models of human performance used in HRA. This paper introduces a methodology for carefully combining multiple sources of empirical data with validated theoretical models to enhance both qualitative and quantitative HRA applications. The methodology uses a comprehensive set of performance influencing factors to combine data from different sources. Further, the paper describes how to use data to gather insights into the relationships among performance influencing factors and to build a quantitative HRA causal model. To illustrate how the methodology is applied, we introduce the Bayesian network model that resulted from applying the methodology to two sources of human performance data from nuclear power plant operations. The proposed model is introduced to demonstrate how to develop causal insights from HRA data and how to incorporate these insights into a quantitative HRA model. The methodology in this paper provides a path forward for carefully incorporating emerging sources of human performance data into an improved HRA method. The proposed model is a starting point for the next generation of data-informed, theoretically-validated HRA methods.