Artículos (Ingeniería y Ciencia de los Materiales y del Transporte)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11377

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Artificial neural networks applied to the measurement of lateral wheel-rail contact force: A comparison with a harmonic cancellation method
(Elsevier, 2020-11) Urda Gómez, Pedro; Fernández Aceituno, Javier; Muñoz Moreno, Sergio; Escalona Franco, José Luis; Universidad de Sevilla. Departamento de Ingeniería Mecánica y de Fabricación; Universidad de Sevilla. Departamento de Ingeniería y Ciencia de los Materiales y del Transporte; Conserjería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía; Universidad de Sevilla. TEP111: Ingeniería Mecánica
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.
Estimation of lateral track irregularity using a Kalman filter. Experimental validation
(Elsevier, 2021-07) Muñoz Moreno, Sergio; Ros, Javier; Urda Gómez, Pedro; Escalona Franco, José Luis; Universidad de Sevilla. Departamento de Ingeniería Mecánica y de Fabricación; Universidad de Sevilla. Departamento de Ingeniería y Ciencia de los Materiales y del Transporte; Conserjería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía; Universidad de Sevilla. TEP111: Ingeniería Mecánica
The aim of this work is the development of a model-based methodology for the estimation of lateral track irregularities from measurements from different sensors mounted on an in-service vehicle: a gyroscope to measure wheelset yaw angular velocity, two accelerometers to measure lateral acceleration of the wheelset and bogie frame, and an encoder to obtain forward velocity of the vehicle. The proposed methodology is based on the Kalman filtering technique, through the use of a highly simplified linear dynamic model of a bogie, capable of capturing the most relevant lateral dynamic behaviour of the entire vehicle. The simplified dynamic model (SM) is based on a vehicle running at variable forward velocity on a track, which comprises straight, curve and transition sections. Finally, the proposed methodology has been experimentally validated through an experimental campaign carried out in a 90 m 1:10 scaled track facility at the University of Seville and an instrumented scaled vehicle. The results of the estimation of the lateral alignment are analysed in the space domain and in the space frequency domain, according to standards. These results are promising, showing a good performance for monitoring lateral alignment on straight and curve tracks, with a very low computational cost. Only in the case of very sharp curves, when continuous flange contact takes place, the estimator is not able to precisely estimate lateral alignment.

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Examinando Artículos (Ingeniería y Ciencia de los Materiales y del Transporte) por Agencia financiadora "Conserjería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía"