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GGC:Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train
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作者 Jie Man Honghui Dong +1 位作者 Limin Jia Yong Qin 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2022年第1期207-222,共16页
Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from... Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks. 展开更多
关键词 vehicle information network structure mining gray theory Granger causality theory complex network theory high-speed train
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