吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (1): 63-69.doi: 10.13229/j.cnki.jdxbgxb20200730
Gui-xiang SHEN(),Lan LUAN,Ying-zhi ZHANG(),Li-ming MU,Shu-bin LIANG
摘要:
为评估加工中心组件的故障传播影响力,快速、准确地识别系统关键组件,有效控制故障传播,采用故障传播机理分析与有向图结合的方式表征组件间故障传播路径。运用DWNodeRank算法评估组件间故障影响度,并结合故障率指标计算组件的故障传播率。考虑有效可达路径,基于改进ASP算法计算故障传播影响力值,实现对组件故障传播影响力的评估。实例分析和结果表明:DWNodeRank算法考虑了故障传播的方向和传播强度,有效降低了迭代复杂性并准确评估了节点影响度;故障传播率作为故障传播影响力评估的依据,其动态时变特征使得评估结果实时、精确,对于加工中心健康维护具有重要意义。
中图分类号:
1 | Muchnik L, Aral S, Taylor S J. Social influence bia:a randomized experiment[J]. Science,2013, 341(6146):647-651. |
2 | Morone F, Makse H A. Influence maximization in complex networks through optimal percolation[J]. Nature, 2015, 524(7563):65-68. |
3 | Freeman L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1):35-41. |
4 | Poulin R, Boily M C, Mâsse B R. Dynamical systems to define centrality in social networks[J]. Social Networks, 2000, 22(3):187-220. |
5 | Chen D B, Gao H, Zhou T, et al. Identifying influential nodes in large-scale directed networks:the role of clustering[J]. PLoS One, 2013, 8(10):No. e77455. |
6 | Brin S, Page L. The anatomy of a large-scale hypertextual web search engine[J]. Computer Networks and ISDN Systems, 1998, 30(1-7):107-117. |
7 | 胡庆成,尹龑燊,马鹏斐,等. 一种新的网络传播中最有影响力的节点发现算法[J]. 物理学报, 2013, 62(14):9-19. |
Hu Qing-cheng,Yin Yan-shen,Ma Peng-fei,et al. A new approach to identify influential spreaders in complex networks[J]. Acta Physica Sinica, 2013, 62(14):9-19. | |
8 | 刘影. 复杂网络中节点影响力挖掘及其应用研究[D]. 成都:电子科技大学航空航天学院, 2016. |
Liu Ying. Research on ranking the influence of nodes on complex networks and its application[D]. Chengdu:School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, 2016. | |
9 | Kitsak M, Gallos L K, Havlin S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6: 888-893. |
10 | Bao Z K, Ma C, Xiang B B, et al. Identification of influential nodes in complex networks:method from spreading probability viewpoint[J]. Physica A: Statistical Mechanics and its Applications, 2017, 468: 391-397. |
11 | 阮逸润,老松杨,王竣德,等. 一种改进的基于信息传播率的复杂网络影响力评估算法[J]. 物理学报,2017, 66(20):312-322. |
Ruan Yi-run, Lao Song-yang, Wang Jun-de, et al. An improved evaluating method of node spreading influence in complex network based on information spreading probability[J]. Acta Physica Sinica, 2017, 66(20):312-322. | |
12 | 赵之滢, 于海, 朱志良, 等. 基于网络社团结构的节点传播影响力分析[J]. 计算机学报, 2014, 37(4):753-766. |
Zhao Zhi-ying, Yu Hai, Zhu Zhi-liang, et al. Identifying influential spreaders based on network community structure[J]. Chinese Journal of Computers, 2014, 37(4):753-766. | |
13 | Morone F, Makse H A. Influence maximization in complex networks through optimal percolation[J]. Nature, 2015, 524(7563): 65-68. |
14 | 胡满玉. 基于链接关系的有向加权复杂网络关键节点识别技术研究[D]. 南京:南京理工大学计算机科学与工程学院, 2012. |
Hu Man-yu. Research on key node recognition technology of directed weighted complex network based on link relationship[D]. Nan Jing:School of Computer Science and Engineering, Nanjing University of Science and Technology, 2012. | |
15 | 李庆扬,王能超,易大义. 数值分析[M]. 北京:清华大学出版社, 2008. |
[1] | 杨兆军, 杨川贵, 陈菲, 郝庆波, 郑志同, 王松. 基于PSO算法和SVR模型的加工中心可靠性模型参数估计[J]. 吉林大学学报(工学版), 2015, 45(3): 829-836. |
[2] | 王晓峰, 申桂香, 张英芝, 谷东伟, 李怀洋, 刘葳. 基于改进危害度和DEMATEL方法的 abc轴进给系统的故障排序[J]. 吉林大学学报(工学版), 2012, 42(01): 122-127. |
[3] | 王晓峰, 申桂香, 张英芝, 张立敏, 王志琼, 刘葳. 加工中心可靠性与维修性影响度仿真分析[J]. 吉林大学学报(工学版), 2011, 41(增刊1): 160-163. |
[4] | 王晓峰,申桂香,张英芝,陈炳锟,李怀洋. 基于群体决策和多种赋值方式的加工中心关键部件RPN分析[J]. 吉林大学学报(工学版), 2011, 41(6): 1630-1635. |
|