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J.C. Jauregui-Correa, J. Rodríguez-Resendis, M. Romo-Aviles, L. Morales-Velazquez, G. Hurtado-Hurtado and T. Sandoval-Valencia

Autonomous University of Queretaro, School of Enigneering, Mexico

doi:10.4203/ccc.7.22.4

J.C. Jauregui-Correa, J. Rodríguez-Resendis, M. Romo-Aviles, L. Morales-Velazquez, G. Hurtado-Hurtado, T. Sandoval-Valencia, "Predicting Wheel Slippage in Railways using Bidirectional Recurrent Neural Networks", 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 22.4, 2024, doi:10.4203/ccc.7.22.4

Keywords: neural networks, artificial intelligence, wheel wear, slippage, experimental data, condition monitoring.

Abstract

This paper shows the application of Artificial Intelligence to predict wheel slippage in railways. The prediction was conducted with a Bidirectional Recurrent Neural Network, and the data was produced using an experimental railcar and track. The objective is to predict slippage by only measuring the train acceleration without recording the train’s speed and wheel velocity. Eighty experimental data were applied to train and validate the Neural Network, and the results show that it was possible to predict slippage using only the train’s acceleration data. The method presented in this paper can be used to design a monitoring system and predict wheel wear and polygonization.

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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 22.4 Predicting Wheel Slippage in Railways using Bidirectional Recurrent Neural Networks J.C. Jauregui-Correa, J. Rodríguez-Resendis, M. Romo-Aviles, L. Morales-Velazquez, G. Hurtado-Hurtado and T. Sandoval-Valencia Autonomous University of Queretaro, School of Enigneering, Mexico doi:10.4203/ccc.7.22.4 Full Bibliographic Reference for this paper J.C. Jauregui-Correa, J. Rodríguez-Resendis, M. Romo-Aviles, L. Morales-Velazquez, G. Hurtado-Hurtado, T. Sandoval-Valencia, "Predicting Wheel Slippage in Railways using Bidirectional Recurrent Neural Networks", 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 22.4, 2024, doi:10.4203/ccc.7.22.4 Keywords: neural networks, artificial intelligence, wheel wear, slippage, experimental data, condition monitoring. Abstract This paper shows the application of Artificial Intelligence to predict wheel slippage in railways. The prediction was conducted with a Bidirectional Recurrent Neural Network, and the data was produced using an experimental railcar and track. The objective is to predict slippage by only measuring the train acceleration without recording the train’s speed and wheel velocity. Eighty experimental data were applied to train and validate the Neural Network, and the results show that it was possible to predict slippage using only the train’s acceleration data. The method presented in this paper can be used to design a monitoring system and predict wheel wear and polygonization. download the full-text of this paper (PDF, 11 pages, 862 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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