Towards autonomous health monitoring of rails using a FEA-ANN based approach

Brown, L, Afazov, S ORCID: https://orcid.org/0000-0001-5346-1933 and Scrimieri, D, 2022. Towards autonomous health monitoring of rails using a FEA-ANN based approach. In: Jansen, T, Jensen, R, Mac Parthaláin, N and Lin, C-M, eds., Advances in computational intelligence systems: contributions presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK. Advances in intelligent systems and computing (1409). Cham: Springer International Publishing, pp. 569-576. ISBN 9783030870935

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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. 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.

Item Type:	Chapter in book
Creators:	Brown, L., Afazov, S. and Scrimieri, D.
Publisher:	Springer International Publishing
Place of Publication:	Cham
Date:	2022
Number:	1409
ISBN:	9783030870935
Identifiers:	Number Type 10.1007/978-3-030-87094-2_50 DOI 1497691 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	22 Nov 2021 16:15
Last Modified:	18 Nov 2022 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/44923

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