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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Westlake, J.: Network rail limited annual report and accounts (2009). https://cdn.networkrail.co.uk/wp-content/uploads/2019/07/Annual-report-and-accounts-2019-Chief-financial-officers-review.pdf. Accessed 16 June 2021
Robinson, M.: Fatigue in Railway Infrastructure, 1st edn. Elsevier, Amsterdam (2009)
Frost, M.: Railway management and engineering. Proc. Inst. Civ. Eng. – Transp. 169(2), 121–121 (2016)
Chen, G.: Friction-induced vibration of a railway wheelset-track system and its effect on rail corrugation. Lubricants 8(2), 18 (2020)
Al Nageim, H., Mohammed, F., Lesley, L.: Numerical results of the LR55 track system modelled as multilayer beams on elastic foundation. J. Constr. Steel Res. 46(1–3), 347 (1998)
Povilaitienė, I., Podagėlis, I.: Research into rail side wearing on curves. Transport 18(3), 124–129 (2003)
Peran, Z.: Dynamic forces between the rails and the wheels of railway vehicle. PROMET - Traffic Transp. 28(1), 63–69 (2016)
Lewis, R., Olofsson, U.: Wheel-Rail Interface Handbook. Woodhead Publishing Limited (2009)
Sariev, E., Germano, G.: Bayesian regularised artificial neural networks for the estimation of the probability of default. Quantit. Financ. 20(2), 311–328 (2019)
Krummenacher, G., Ong, C.S., Koller, S., Kobayashi, S., Buhmann, J.M.: Wheel defect detection with machine learning. IEEE Trans. Intell. Transp. Syst. 19(4), 1176–1187 (2018)
Kianifar, M.R., Campean, F.: Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide. Struct. Multidiscip. Optim. 61(1), 159–186 (2019). https://doi.org/10.1007/s00158-019-02352-1
Vo, K.D., Tieu, A.K., Zhu, H.T., Kosasih, P.B.: A 3D dynamic model to investigate wheel-rail contact under high and low adhesion. Int. J. Mech. Sci. 85, 63–75 (2014)
Network Rail, Network Statement 2019. https://www.networkrail.co.uk/wp-content/uploads/2019/03/Network-Statement-2019.pdf. Accessed 16 June 2021
Burden, F., Winkler, D.: Bayesian regularization of neural networks. In: Livingstone, D.J. (ed.) Artificial Neural Networks. Methods in Molecular BiologyTM, vol. 458. Humana Press (2008)
Reed, R.D.: Neural Smithing. MIT Press, Cambridge (1999)
Uzair, M., Jamil, N.: Effects of hidden layers on the efficiency of neural networks. In: IEEE 23rd International Multitopic Conference (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Brown, L., Afazov, S., Scrimieri, D. (2022). Towards Autonomous Health Monitoring of Rails Using a FEA-ANN Based Approach. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-87094-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87093-5
Online ISBN: 978-3-030-87094-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)