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The analysis of common cause failure based on impact vector considering human factor diversity
Liu J(刘捷); Wang K(王锴); Xu AD(徐皑冬); Wang H(王宏)
作者部门工业控制网络与系统研究室
会议名称2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013
会议日期June 19-21, 2013
会议地点Melbourne, VIC, Australia
会议主办者Swinburne University of Technology
会议录名称Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013
出版者IEEE Computer Society
出版地Washington, United States
2013
页码1733-1738
收录类别EI ; CPCI(ISTP)
EI收录号20133416634114
WOS记录号WOS:000326679200318
产权排序1
ISBN号978-1-4673-6321-1
关键词Errors Fuzzy Logic Industrial Electronics Reliability Analysis
摘要In the redundant system, Common Cause Failure (CCF) can result in failures of more than one module. Human Error is one of the most important causes among all the causes of CCF. Human factor diversity is an effective method against CCF due to human error. Although there is clear evidence that diversity can bring benefits, these benefits can be difficult to quantify. The current methods of human reliability analysis (HRA) almost never take into account the effect of human factor diversity on system reliability. Therefore a new method which can quantify the benefits of human factor diversity from the component degradation state point of view is proposed in this paper. The key idea of the new method is to build a fuzzy logic model based on an extended CREAM for the estimation of the probability of an erroneous human action which is then mapped into component degradation valve (CDV). The relationship between CCF rate and human factor diversity is studied by performing CCF analysis based on impact vectors which can be calculated from CDV. Finally, an illustrative example is presented, which shows the application process of the novel method.
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/12434
专题工业控制网络与系统研究室
推荐引用方式
GB/T 7714
Liu J,Wang K,Xu AD,et al. The analysis of common cause failure based on impact vector considering human factor diversity[C]//Swinburne University of Technology. Washington, United States:IEEE Computer Society,2013:1733-1738.
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