@article{Man2022, 
author = {Jie Man and Honghui Dong and Limin Jia and Yong Qin},
title = {GGC: Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train},
year = {2022},
journal = {Tsinghua Science and Technology},
volume = {27},
number = {1},
pages = {207-222},
keywords = {high-speed train, vehicle information network, structure mining, gray theory, Granger causality theory, complex network theory},
url = {https://www.sciopen.com/article/10.26599/TST.2021.9010034},
doi = {10.26599/TST.2021.9010034},
abstract = {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.}
}