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A TECHNIQUE FOR SCALING OF DECISION ERROR OPPORTUNITIES BASED ON SITUATIONAL FEATURES IDENTIFIED FROM OPERATIONAL EVENTS

机译:一种基于操作事件识别的情境特征的决策误差机会的技术

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Much information is available from operating experience about contexts under the condition of a decision-based error of commission (EOC). Illustrated by a set of 26 operational EOC cases, this paper presents a data analysis technique utilizing this information for the compilation of an interval scale that correlates with the EOC likelihood. In addition, conclusions for achieving advances in human reliability analysis (HRA) are drawn. The cases are analyzed with a framework of two types of factors: causal factors (e.g. misleading indication) which relate to the initial motivation (or consideration) of the inappropriate action, and influencing factors related to situational features (e.g. availability of backup indication) that mediate the impact of a causal factor on the EOC likelihood. Based on comparative assessments (supported by the evaluation results for these factors), a scale of reliability indices with six discrete categories is finally obtained. On this scale, an index of i=0 represents the worst case (error-forcing effect extremely high), and high indices are corresponding to low EOC probabilities. Since the 26 EOC cases appear as references points on this scale, the scale can be used to estimate the reliability index of a 'new' EOC case on the basis of a comparison of patterns of causal and influencing factors. Furthermore, the obtained distribution of contexts (with different reliability indices) under the condition of an EOC can be utilized for a Bayesian estimation of conditional EOC probabilities. The paper discusses as well problems concerning the proposed scaling technique, namely (1) difficulties in performing comparisons with different causal factors involved, (2) hidden effects of factors not addressed so far, and (3) dependencies between comparisons.
机译:许多信息可从(EOC)佣金的基础的决策错误的情况下运行有关环境的经验。通过一组26操作EOC例中所示,本文提出利用该信息的间隔尺度的汇编数据分析技术,该技术与EOC可能性相关因素。此外,为了实现在人的可靠性分析(HRA)的进步结论。的情况下进行了分析两种类型的因素的框架:偶然因素(例如误导指示),其涉及不适当的动作的初始动机(或考虑),并且影响相关的情境特征因素(例如备份指示的可用性),该调解的致病因素对EOC可能性的影响。基于比较的评估(通过评价结果这些因素支持),最终获得具有六个分立的类别可靠性指标的标度。这种规模的,我的索引= 0表示最坏的情况下(错误迫使效果极高),和高指数对应于低EOC概率。由于26 EOC例显示为这种规模的引用点,规模可被用来估计的因果的模式及影响因素的比较的基础上的“新” EOC壳体的可靠性指标。此外,上下文的EOC的条件下获得的分布(具有不同的可靠性指标)可用于条件EOC概率的贝叶斯估计。论述作为关于所提出的缩放技术,在执行涉及不同的偶然因素比较即(1)的困难,(2)隐藏的因素的影响到目前为止还没有解决,并且比较(3)之间的依赖性以及问题。

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