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How Many Performance Shaping Factors are Necessary for Human Reliability Analysis?

机译:人类可靠性分析需要多少个性塑造因子?

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It has been argued that human reliability analysis (HRA) has expended considerable energy on creating detailed representations of human performance through an increasingly long list of performance shaping factors (PSFs). It is not clear, however, to what extent this refinement and expansion of PSFs has enhanced the quality of HRA. Indeed, there is considerable range in the number of PSFs provided by individual HRA methods, ranging from single factor models such as timereliability curves, up to 50 or more PSFs in some current HRA models. The US Nuclear Regulatory Commission advocates 15 PSFs in its HRA Good Practices (NUREG-1792), while its SPAR-H method (NUREG/CR-6883) espouses the use of eight PSFs and its ATHEANA method (NUREG-1624) features an open-ended number of PSFs. The apparent differences in the optimal number of PSFs can be explained in terms of the diverse functions of PSFs in HRA. The purpose of this paper is to explore the role of PSFs across different stages of HRA, including identification of potential human errors, modeling of these errors into an overall probabilistic risk assessment, quantifying errors, and preventing errors.
机译:有人认为,人类可靠性分析(HRA)通过越来越长的性能塑造因素(PSF)创造了人类绩效的详细说明来消耗了相当大的能量。然而,目前尚不清楚,这一细化和扩张PSF的扩张增强了HRA的质量。实际上,各个HRA方法提供的PSF的数量有相当大的范围,从单因素模型等单因素模型等等,在一些当前的HRA模型中高达50个或更多的PSF。美国核监管委员会在其HRA良好做法(NUREG-1792)提倡15个PSF,而其SPAR-H方法(NUREG / CR-6883)支持使用八个PSF及其ATHEANA方法(NUREG-1624)的功能开放 - PSF的数量。可以在HRA中PSFS的不同功能来解释PSF的最佳数量的表观差异。本文的目的是探讨PSFS在HRA不同阶段的作用,包括识别潜在的人为错误,这些错误建模到整体概率风险评估,量化误差和防止错误。

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