Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems

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dc.contributor.authorDurazo-Cardenas, Isidro
dc.contributor.authorStarr, Andrew G.
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorBevilacqua, Maurizio
dc.contributor.authorMorineau, Julien
dc.date.accessioned2016-10-23T09:59:14Z
dc.date.available2016-10-23T09:59:14Z
dc.date.issued2014-10-31
dc.description.abstractThe rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance. The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely. Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either. Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed.en_UK
dc.identifier.citationI. Durazo-Cardenas, A. Starr, A. Tsourdos, M. Bevilacqua, J. Morineau, Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems. 3rd International Conference on Through-Life Engineering Services, Cranfield, 4-5 November 2014, Cranfield, Cranfield University, UK. Procedia CIRP, Volume 22, 2014, Pages 219-224en_UK
dc.identifier.issn2212-8271
dc.identifier.urihttp://dx.doi.org/10.1016/j.procir.2014.07.002.
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10835
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 3.0 Internationalen_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectVehicle locationen_UK
dc.subjectUncertaintyen_UK
dc.subjectData fusion architectureen_UK
dc.titlePrecise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systemsen_UK
dc.typeArticleen_UK

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