Intelligent condition monitoring of railway catenary systems: A Bayesian Network approach

Hongrui Wang, Alfredo Nunez, Rolf Dollevoet, Zhigang Liu, Junwen Chen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head vertical acceleration and pantograph-catenary contact force, as inputs
based on their physical meanings and correlations. It outputs an integrated indicator of catenary condition level. The BN parameters are learned from historical measurement data. Preliminary results shows the applicable ability of the BN to integrate multiple types of parameter while make sense of the output to facilitate maintenance decision making.
Original languageEnglish
Title of host publicationProceedings of the 25th International Symposium on Dynamics of Vehicles on Roads and Tracks
Subtitle of host publicationRockhampton, Queensland, Australia, August 14-18, 2017
EditorsMaksym Spiryagin, Timothy Gordon, Colin Cole, Tim McSweeney
Number of pages6
Publication statusPublished - 2017
Event25th International Symposium on Dynamics of Vehicles on Roads and Tracks - Rockhampton, Australia
Duration: 14 Aug 201718 Aug 2017
Conference number: 25
http://www.iavsd2017.org/

Conference

Conference25th International Symposium on Dynamics of Vehicles on Roads and Tracks
Abbreviated titleIAVSD 2017
CountryAustralia
CityRockhampton
Period14/08/1718/08/17
Internet address

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