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    Towards Autonomous Health Monitoring of Rails Using a FEA-ANN Based Approach

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    Publication date
    2021-11-18
    End of Embargo
    2022-11-18
    Author
    Brown, L.
    Afazov, S.
    Scrimieri, Daniele
    Keyword
    Rail monitoring
    Finite element analysis (FEA)
    Artificial neural network (ANN)
    Optical scanning
    Stress field prediction
    Rights
    © 2022 Springer. Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    embargoedAccess
    
    Metadata
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    Abstract
    The 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.
    URI
    http://hdl.handle.net/10454/18859
    Version
    Accepted manuscript
    Citation
    Brown 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.
    Link to publisher’s version
    https://doi.org/10.1007/978-3-030-87094-2_50
    Type
    Conference paper
    Notes
    The full text will be available at the end of the publisher's embargo: 18th Nov 2022
    Collections
    Engineering and Informatics Publications

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