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Study on operator's SA reliability in digital NPPs. Part 2: Data-driven causality model of SA

机译:研究数字NPP中运营商的SA可靠性。第2部分:SA的数据驱动因果关系模型

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摘要

This paper tries to overcome shortcomings of the traditional assessment method of situation awareness (SA) reliability and build more robust causality model of SA. Firstly, the organization-oriented analysis framework or method of SA error was used to analyze human factor events in nuclear power plants (NPPs) and the data of 132 samples were obtained. Then, the correlation analysis method is used to identify the correlation relationships between factors influencing SA and next factor analysis method is used to identify the scenes triggering SA error. It includes: operator's mental level, operator's work attitude, stress level and system situation display level. Finally, based on the study of the results highlighted above, a data-driven SA causality model is established. These results shows that, the data-based SA causality model can identify the scenes triggering SA error, and it is very useful to improve the accuracy of quantitative assessment of SA reliability because of considering the causality relationships of performance shaping factors (PSFs). (C) 2017 Elsevier Ltd. All rights reserved.
机译:本文试图克服传统的态势感知(SA)可靠性评估方法的不足,并建立更健壮的SA状态因果模型。首先,采用面向组织的SA错误分析框架或方法来分析核电厂的人为因素事件,获得132个样本的数据。然后,采用相关分析法确定影响SA的因素之间的相关关系,然后采用下一个因素分析法确定触发SA错误的场景。它包括:操作员的心理水平,操作员的工作态度,压力水平和系统状况显示水平。最后,基于对上述结果的研究,建立了一个数据驱动的SA因果关系模型。这些结果表明,基于数据的SA因果关系模型可以识别触发SA错误的场景,并且由于考虑了性能塑造因素(PSF)的因果关系,对于提高SA可靠性定量评估的准确性非常有用。 (C)2017 Elsevier Ltd.保留所有权利。

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