Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements Haghbin, Masoud Chiachío Ruano, Juan Muñoz, Sergio Escalona Franco, Jose Luis Guillén, Antonio J. Crespo Marquez, Adolfo Cantero Chinchilla, Sergio Rail corrugation Deep learning Convolutional neural networks This paper presents a deep learning approach for predicting rail corrugation based on onboard rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways. 2024-09-05T11:09:00Z 2024-09-05T11:09:00Z 2024-07-17 journal article Haghbin, M.; Chiachío, J.; Muñoz, S.; Escalona Franco, J.L.; Guillén, A.J.; Crespo Marquez, A.; Cantero-Chinchilla, S. Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements. Sensors 2024, 24, 4627. https://doi.org/10.3390/s24144627 https://hdl.handle.net/10481/94016 10.3390/s24144627 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI