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首页> 外文期刊>Annals of nuclear energy >A data mining framework within the Chinese NPPs operating experience feedback system for identifying intrinsic correlations among human factors
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A data mining framework within the Chinese NPPs operating experience feedback system for identifying intrinsic correlations among human factors

机译：中国核电厂运行经验反馈系统中的数据挖掘框架，用于识别人为因素之间的内在联系

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摘要

With the continuous increase in the number of operating nuclear power plants (NPPs) in China, the amount of operating experience feedback (OEF) increases significantly. On the other hand, the safe operation of NPP5 has become an urgent problem that the National Nuclear Safety Administration (NNSA) must solve. To this end, NNSA established a nationalwide OEF system to improve the safety level of NPPs and strengthen the exchange of operating experience. Analyzing the human factors events (HFEs) is an important part of OEF and it is significant to improve human performance and prevent human error. Data mining has been recognized as an effective way to analyze data. With the continuous increase in operating event reports, data mining related to nuclear safety becomes a new domain of study. In this paper, we propose a data mining framework in support of the OEF system. The framework combines three statistical approaches (i.e., correlation analysis, cluster analysis and association rule mining) for identifying intrinsic correlations among human factors: correlation analysis measures the strength of linear relationship between human factors; cluster analysis classifies human factors into relevant groups; association rule mining identifies associations and causalities among human factors. For illustration, we apply the proposed framework to 162 human factors events (screened out from 313 events collected from the OEF system), and the results reflect the feasibility and effectiveness of the framework in identifying the intrinsic correlations among human factors. Besides, further suggestions for improving human performance and preventing human errors in NPP5 are also discussed. (C) 2018 Elsevier Ltd. All rights reserved.
机译：随着中国在运核电厂（NPP）数量的不断增加，在运经验反馈（OEF）的数量显着增加。另一方面，NPP5的安全运行已成为国家核安全局（NNSA）必须解决的紧迫问题。为此，国家核安全局建立了全国范围的OEF系统，以提高核电厂的安全水平并加强交流操作经验。分析人为因素事件（HFE）是OEF的重要组成部分，对于提高人的绩效和防止人为错误具有重要意义。数据挖掘已被认为是分析数据的有效方法。随着运行事件报告的不断增加，与核安全有关的数据挖掘成为一个新的研究领域。在本文中，我们提出了一个支持OEF系统的数据挖掘框架。该框架结合了三种统计方法（即相关分析，聚类分析和关联规则挖掘），以识别人为因素之间的内在联系：相关性分析衡量人为因素之间线性关系的强度;聚类分析将人为因素分为相关组；关联规则挖掘可识别人为因素之间的关联和因果关系。为了说明这一点，我们将建议的框架应用于162个人为因素事件（从OEF系统收集的313个事件中筛选出来），结果反映了该框架在识别人为因素之间的内在联系方面的可行性和有效性。此外，还讨论了在NPP5中改善人员绩效和防止人为错误的其他建议。 （C）2018 Elsevier Ltd.保留所有权利。

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