Article
Artificial neural networks applied to the measurement of lateral wheel-rail contact force: A comparison with a harmonic cancellation method
Author/s | Urda Gómez, Pedro
Fernández Aceituno, Javier Muñoz Moreno, Sergio Escalona Franco, José Luis |
Department | Universidad de Sevilla. Departamento de Ingeniería Mecánica y de Fabricación Universidad de Sevilla. Departamento de Ingeniería y Ciencia de los Materiales y del Transporte |
Publication Date | 2020-11 |
Deposit Date | 2024-08-28 |
Published in |
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Abstract | This paper presents a method for the experimental measurement of the lateral wheel-rail contact force based on Artificial Neural Networks (ANN). It is intended to demonstrate how an Artificial Intelligence (AI) method ... This paper presents a method for the experimental measurement of the lateral wheel-rail contact force based on Artificial Neural Networks (ANN). It is intended to demonstrate how an Artificial Intelligence (AI) method proves to be a valid alternative to other approaches based on sophisticated mathematical models when it is applied to the wheel-rail contact force measurement problem. This manuscript addresses the problem from a computational and experimental approach. The artificial intelligence algorithm has been experimentally tested in a real scenario using a 1:10 instrumented scaled railway vehicle equipped with a dynamometric wheelset running on a 5-inch-wide track. The obtained results show that the ANN approach is an easy and computationally efficient method to measure the applied lateral force on the instrumented wheel that requires the use of fewer sensors. |
Funding agencies | Conserjería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía |
Project ID. | US-1257665 |
Citation | Urda, P., Aceituno, J.F., Muñoz, S. y Escalona, J.L. (2020). Artificial neural networks applied to the measurement of lateral wheel-rail contact force: A comparison with a harmonic cancellation method. Mechanism and Machine Theory, 153, 103968. https://doi.org/10.1016/j.mechmachtheory.2020.103968. |
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