Acceleration-based friction coefficient estimation of a rail vehicle using feedforward NN: validation with track measurements

Abstract

Low friction can lead to poor adhesion conditions between the rail and wheel, which is detrimental to rail vehicle operation and safety. Up to date knowledge of the rail-wheel friction level is currently not available across rail networks, meaning planning mitigation strategies is difficult. This paper presents a real-time friction coefficient estimation algorithm based on a feed-forward neural network (FNN). Unlike conventional methods, the FNN does not depend on slip/adhesion curves or creep force models, and only requires wheelset longitudinal acceleration and speed. The wheelset acceleration and friction measurements are obtained by running a two-car rail vehicle on a friction-modified track with five different levels of friction conditions at four different vehicle speeds. Four different FNNs are trained for four speed conditions, and their estimation performance were validated by training multiple FNNs and testing them in each speed case using new sets of data. Validation results show that the average mean absolute errors from the four FNNs remains below 0.0083.

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Item Type:	Article
Authors/Creators:	Abduraxman, B. Hubbard, P. https://orcid.org/0000-0003-2730-2538 Harrison, T. Ward, C. Fletcher, D. https://orcid.org/0000-0002-1562-4655 Lewis, R. https://orcid.org/0000-0002-4300-0540 White, B. https://orcid.org/0000-0002-9653-9945
Copyright, Publisher and Additional Information:	© 2024 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in Vehicle System Dynamics is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Keywords:	Low adhesion detection; friction coefficient estimation; slip; creep; railhead conditioning
Dates:	Published: 4 March 2024 Published (online): 4 March 2024 Accepted: 21 February 2024 Submitted: 29 September 2023
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:	Funder Grant number NETWORK RAIL LIMITED UNSPECIFIED
Depositing User:	Symplectic Sheffield
Date Deposited:	04 Jul 2024 07:55
Last Modified:	08 Nov 2024 15:52
Status:	Published
Publisher:	Informa UK Limited
Refereed:	Yes
Identification Number:	10.1080/00423114.2024.2323600
Related URLs:	Author
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:214339

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Acceleration-based friction coefficient estimation of a rail vehicle using feedforward NN: validation with track measurements

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