Intelligent condition monitoring of railway catenary systems

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

Wang, H. (TU Delft Railway Engineering)
Nunez, Alfredo (TU Delft Railway Engineering)
Dollevoet, R.P.B.J. (TU Delft Railway Engineering)
Liu, Zhigang (Southwest Jiaotong University)
Chen, Junwen (Southwest Jiaotong University)

Spiryagin, Maksym (editor)
Gordon, Timothy (editor)
Cole, Colin (editor)
McSweeney, Tim (editor)

2017

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.

http://resolver.tudelft.nl/uuid:9437c6cd-f33f-4317-9020-82f05da5f1b6

Proceedings of the 25th International Symposium on Dynamics of Vehicles on Roads and Tracks: Rockhampton, Queensland, Australia, August 14-18, 2017

25th International Symposium on Dynamics of Vehicles on Roads and Tracks, 2017-08-14 → 2017-08-18, Rockhampton, Australia

Institutional Repository

conference paper

© 2017 H. Wang, Alfredo Nunez, R.P.B.J. Dollevoet, Zhigang Liu, Junwen Chen