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公开(公告)号:US20170015339A1
公开(公告)日:2017-01-19
申请号:US15123684
申请日:2015-11-27
发明人: Limin JIA , Yong QIN , Yanhui WANG , Shuai LIN , Hao SHI , Lifeng BI , Lei GUO , Lijie LI , Man LI
CPC分类号: B61L99/00 , B61L27/0055 , B61L27/0083 , G06F17/50 , G06N99/005 , H04L67/12
摘要: The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.
摘要翻译: 本发明公开了一种基于复杂网络的高速列车系统安全评估方法。 该方法包括以下步骤:(1)构建高速列车系统物理结构的网络模型,并根据网络模型构建节点的功能属性度; (2)提取组件故障之间的功能属性度,故障率和平均时间作为输入量,使用LIBSVM软件进行SVM训练; (3)进行加权kNN-SVM判断:判断为不可分类的采样点,以获得高速列车系统的安全等级。 对于具有复杂的物理结构和操作条件的高速列车系统,当系统中的部件的状态改变时,该方法可以评估对系统安全性的影响程度。 实验结果表明该算法精度高,实用性好。