Computational Technology Resources - CCC

B. Abduraxman¹, P. Hubbard¹, T. Harrison¹, C. Ward², D. Fletcher³, R. Lewis³, K. Chandrasekhar⁴ and D. Vincent⁴

¹Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, United Kingdom
²Rail Accident Investigation Board, Derby, United Kingdom
³The Department of Mechanical Engineering, The University of Sheffield, United Kingdom
⁴Hitachi Rail, London, United Kingdom

doi:10.4203/ccc.7.9.3

B. Abduraxman, P. Hubbard, T. Harrison, C. Ward, D. Fletcher, R. Lewis, K. Chandrasekhar, D. Vincent, "Dynamics-Based Estimation of Wheel-Rail Friction Coefficient using Deep CNN", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 9.3, 2024, doi:10.4203/ccc.7.9.3

Keywords: friction coefficient estimation, low adhesion detection, wheel-rail friction, convolutional neural networks, railway, condition monitoring.

Abstract

This paper presents an estimation scheme for rail-wheel friction coefficients applying a multi-channel deep convolutional neural network on axlebox accelerations. Different to conventional approaches, the multi-channel deep convolutional neural network does not depend on any slip or creep measurements, nor knowledge of vehicle parameters. It is trained using axlebox longitudinal and lateral acceleration measurements and known rail friction coefficient measurements obtained from running a rail vehicle on friction-modified tracks with five different friction levels at four different speeds. The experimental test data includes both straight and curved track scenarios, and independent validation data shows that the friction coefficient can be very accurately estimated under normal running conditions in almost all of the validation data sets.

download the full-text of this paper (PDF, 11 pages, 711 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 9.3 Dynamics-Based Estimation of Wheel-Rail Friction Coefficient using Deep CNN B. Abduraxman¹, P. Hubbard¹, T. Harrison¹, C. Ward², D. Fletcher³, R. Lewis³, K. Chandrasekhar⁴ and D. Vincent⁴ ¹Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, United Kingdom ²Rail Accident Investigation Board, Derby, United Kingdom ³The Department of Mechanical Engineering, The University of Sheffield, United Kingdom ⁴Hitachi Rail, London, United Kingdom doi:10.4203/ccc.7.9.3 Full Bibliographic Reference for this paper B. Abduraxman, P. Hubbard, T. Harrison, C. Ward, D. Fletcher, R. Lewis, K. Chandrasekhar, D. Vincent, "Dynamics-Based Estimation of Wheel-Rail Friction Coefficient using Deep CNN", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 9.3, 2024, doi:10.4203/ccc.7.9.3 Keywords: friction coefficient estimation, low adhesion detection, wheel-rail friction, convolutional neural networks, railway, condition monitoring. Abstract This paper presents an estimation scheme for rail-wheel friction coefficients applying a multi-channel deep convolutional neural network on axlebox accelerations. Different to conventional approaches, the multi-channel deep convolutional neural network does not depend on any slip or creep measurements, nor knowledge of vehicle parameters. It is trained using axlebox longitudinal and lateral acceleration measurements and known rail friction coefficient measurements obtained from running a rail vehicle on friction-modified tracks with five different friction levels at four different speeds. The experimental test data includes both straight and curved track scenarios, and independent validation data shows that the friction coefficient can be very accurately estimated under normal running conditions in almost all of the validation data sets. download the full-text of this paper (PDF, 11 pages, 711 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
Back to top	©Civil-Comp Limited 2023 - terms & conditions