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A Graphical Model Based on Performance Shaping Factors for Assessing Human Reliability

机译:基于性能整形因子的图形模型用于评估人的可靠性

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摘要

Human reliability assessment (HRA) is an aspect of risk analysis concerned with identifying, analyzing, and quantifying the causes, contributions, and occurrence of human failures. Applications of existing HRA methods are often domain-specific, and difficult to implement even for experts. Also, due to the lack of empirical data, managing uncertainty is important, if not essential. In view of such limitations, we propose a new and comprehensive HRA methodology acronymed “PRELUDE” (Performance shaping factor-based human REliability assessment using vaLUation-baseD systEms). It is a quantitative and qualitative HRA methodology, applied to railway operations. The qualitative part characterizes a safety critical situation using performance shaping factors (PSFs). The PSFs are identified from domain specific human factors and PSF-based studies. The quantitative proposition is a framework of a graphical model (Valuation-based System) and belief functions theory. Appropriate representation and handling of all types of uncertainties, and combination of conflicting expert opinions is considered in this framework. To aid in the choice of appropriate combination method, combined expert data are discussed and compared using quantitative metrics. PRELUDE allows quantifying a human failure event given an operational context. Sensitivity analysis is used to establish a priority ranking among the PSFs. Finally, application on a railway accident scenario describes usage and applicability of our proposition.
机译:人的可靠性评估(HRA)是风险分析的一个方面,涉及识别,分析和量化人为失败的原因,贡献和发生情况。现有HRA方法的应用通常是特定于领域的,即使对于专家也难以实现。另外,由于缺乏经验数据,不确定性的管理很重要,即使不是必须的。鉴于此类限制,我们提出了一种新的,全面的HRA方法,缩写为“ PRELUDE”(使用基于价值的系统来基于性能整形因子的人类可靠性评估)。这是一种定量和定性的HRA方法,适用于铁路运营。定性部分使用性能整形因子(PSF)来表征安全关键情况。从领域特定的人为因素和基于PSF的研究中确定PSF。定量命题是图形模型(基于评估的系统)和信念函数理论的框架。在此框架中考虑了适当表示和处理所有类型的不确定性,以及相互矛盾的专家意见。为了帮助选择合适的组合方法,使用定量指标对组合的专家数据进行了讨论和比较。 PRELUDE允许在给定操作环境的情况下量化人为失误事件。灵敏度分析用于在PSF之间建立优先级排序。最后,在铁路事故场景中的应用描述了我们命题的用法和适用性。

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