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dc.contributor.authorBrown, L.
dc.contributor.authorAfazov, S.
dc.contributor.authorScrimieri, Daniele
dc.date.accessioned2022-03-21T09:20:02Z
dc.date.accessioned2022-04-06T14:27:28Z
dc.date.available2022-03-21T09:20:02Z
dc.date.available2022-04-06T14:27:28Z
dc.date.issued2021-11-18
dc.identifier.citationBrown L, Afazov S and Scrimieri D (2021) Towards Autonomous Health Monitoring of Rails Using a FEA-ANN Based Approach. 4th International Engineering Data- and Model-Driven Applications Workshop. In: Jansen T, Jensen R, Mac Parthaláin N and Lin CM (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409, pp 569-576. Springer.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18859
dc.descriptionYesen_US
dc.description.abstractThe current UK rail network is managed by Network Rail, which requires an investment of £5.2bn per year to cover operational costs [1]. These expenses include the maintenance and repairs of the railway rails. This paper aims to create a proof of concept for an autonomous health monitoring system of the rails using an integrated finite element analysis (FEA) and artificial neural network (ANN) approach. The FEA is used to model worn profiles of a standard rail and predict the stress field considering the material of the rail and the loading condition representing a train travelling on a straight line. The generated FEA data is used to train an ANN model which is utilised to predict the stress field of a worn rail using optically scanned data. The results showed that the stress levels in a rail predicted with the ANN model are in an agreement with the FEA predictions for a worn rail profile. These initial results indicate that the ANN can be used for the rapid prediction of stresses in worn rails and the FEA-ANN based approach has the potential to be applied to autonomous health monitoring of rails using fast scanners and validated ANN models. However, further development of this technology would be required before it could be used in the railway industry, including: real time data processing of scanned rails; improved scanning rates to enhance the inspection efficiency; development of fast computational methods for the ANN model; and training the ANN model with a large set of representative data representing application specific scenarios.en_US
dc.language.isoenen_US
dc.publisherSpringer
dc.relation.isreferencedbyhttps://doi.org/10.1007/978-3-030-87094-2_50en_US
dc.rights© 2022 Springer. Reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectRail monitoringen_US
dc.subjectFinite element analysis (FEA)en_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectOptical scanningen_US
dc.subjectStress field predictionen_US
dc.titleTowards Autonomous Health Monitoring of Rails Using a FEA-ANN Based Approachen_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.date.EndofEmbargo2022-11-18
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full text will be available at the end of the publisher's embargo: 18th Nov 2022en_US
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-03-21T09:20:05Z
refterms.dateFOA2022-04-06T14:28:02Z
dc.openaccess.statusembargoedAccessen_US


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