Track geometry deterioration modelling for asset management: a visual analytics approach

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dc.contributor.authorAlotaibi, Abdulaziz
dc.contributor.authorDurazo-Cardenas, Isidro
dc.contributor.authorNamoano, Bernadin
dc.contributor.authorStarr, Andrew
dc.date.accessioned2022-11-08T11:34:48Z
dc.date.available2022-11-08T11:34:48Z
dc.date.issued08/11/2022
dc.description11th International Conference on Through-life Engineering Services - TESConf 2022, 8-9 November 2022, Cranfield UKen_UK
dc.description.abstractTo maintain safe operations and cost-effective maintenance, British railway tracks must be monitored. Track recording assets which include trains and cars, regularly monitor key components of the track in order to detect and diagnose early incipient faults. The measurements accumulate over time, providing time series data that can be used to model track geometry deterioration process. However, the modelling results are often too sophisticated to be used to their full potential in track asset management. As a result, the goal of this research is to use visualisation approaches to display the results of track geometry deterioration, which would simplify and enhance track asset management. Two visual techniques have been used. The first visual includes two dimensional plots enabling visual fault detection and localisation and the second is a 3D plot which gives a better sight for the decision makers to act. These visual analytics allowed a better understanding of fault occurrence, enable a vast amount of data integration, flexible and simple for stakeholders to use. The limitations of such approaches include the inability to visualise more than 5 dimensions and human interpretation.en_UK
dc.description.sponsorshipDMG Morien_UK
dc.identifier.citationAlotaibi A, Durazo Cardenas I, Namoano B, Starr A. (2022) Track geometry deterioration modelling for asset management: a visual analytics approach. In: 11th International Conference on Through-life Engineering Services - TESConf 2022, 8-9 November 2022, Cranfield UK, Paper number 4735en_UK
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18669
dc.language.isoenen_UK
dc.publisherCranfield Universityen_UK
dc.relation.ispartofseriesTESConf2022;4735
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRailwayen_UK
dc.subjectrailway infrastructureen_UK
dc.subjecttrack geometry dataen_UK
dc.subjectrailway asset managementen_UK
dc.subjectdecision makingen_UK
dc.subjecttrack faulten_UK
dc.subjecttwisten_UK
dc.subjectgaugeen_UK
dc.titleTrack geometry deterioration modelling for asset management: a visual analytics approachen_UK
dc.typeConference paperen_UK

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11th International Conference on Through-life Engineering Services 2022