GGC: Gray-granger causality method for sensor correlation network structure mining on high-speed train Article Swipe

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Jie Man , Honghui Dong , Limin Jia , Yong Qin ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.26599/tst.2021.9010034

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.

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Train

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Train Granger causality Computer science Data mining Construct (python library) Causality (physics) Gray (unit) Correlation Point (geometry) Artificial intelligence Real-time computing Machine learning Mathematics Computer network Physics Geometry Cartography Quantum mechanics Geography Medicine Radiology

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Type: article
Language: en
Landing Page: https://doi.org/10.26599/tst.2021.9010034
PDF: https://ieeexplore.ieee.org/ielx7/5971803/9515693/09515793.pdf
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Cited By: 10
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DOI: https://doi.org/10.26599/tst.2021.9010034

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Title: GGC: Gray-granger causality method for sensor correlation network structure mining on high-speed train

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-08-17

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Authors: Jie Man, Honghui Dong, Limin Jia, Yong Qin

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Landing page: https://doi.org/10.26599/tst.2021.9010034

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Concepts: Train, Granger causality, Computer science, Data mining, Construct (python library), Causality (physics), Gray (unit), Correlation, Point (geometry), Artificial intelligence, Real-time computing, Machine learning, Mathematics, Computer network, Physics, Geometry, Cartography, Quantum mechanics, Geography, Medicine, Radiology

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Cited by: 10

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abstract_inverted_index.closely	144
abstract_inverted_index.complex	100
abstract_inverted_index.massive	33
abstract_inverted_index.network	86, 101, 117, 134, 142
abstract_inverted_index.predict	21
abstract_inverted_index.propose	74
abstract_inverted_index.status.	26
abstract_inverted_index.train's	23
abstract_inverted_index.various	11
abstract_inverted_index.vehicle	34, 44, 84, 105, 115, 132
abstract_inverted_index.whether	9
abstract_inverted_index.working	17
abstract_inverted_index.attacks,	138
abstract_inverted_index.attacks.	149
abstract_inverted_index.network.	128
abstract_inverted_index.networks	121
abstract_inverted_index.valuable	30
abstract_inverted_index.Causality	77
abstract_inverted_index.according	54, 63
abstract_inverted_index.addition,	130
abstract_inverted_index.causality	71
abstract_inverted_index.connected	145
abstract_inverted_index.construct	82
abstract_inverted_index.datasets,	53
abstract_inverted_index.determine	8
abstract_inverted_index.difficult	38
abstract_inverted_index.functions	57
abstract_inverted_index.networks,	110
abstract_inverted_index.normally,	18
abstract_inverted_index.operating	25
abstract_inverted_index.subsystem	52, 109, 120, 141
abstract_inverted_index.collection	60, 95
abstract_inverted_index.high-speed	3, 48
abstract_inverted_index.scale-free	127
abstract_inverted_index.components.	61, 96
abstract_inverted_index.correlation	92
abstract_inverted_index.information	1, 31, 85, 106, 116, 133
abstract_inverted_index.Gray-Granger	76
abstract_inverted_index.characteristics	124
cited_by_percentile_year.max	97
cited_by_percentile_year.min	90
countries_distinct_count	1
institutions_distinct_count	4
citation_normalized_percentile.value	0.76958267
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False