Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements
Masoud Haghbin,
Juan Chiachío,
Sergio Muñoz,
Jose Luis Escalona Franco,
Antonio J. Guillén,
Adolfo Crespo Marquez,
Sergio Cantero-Chinchilla
Affiliations
Masoud Haghbin
Department of Structural Mechanics and Hydraulic Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), 18001 Granada, Spain
Juan Chiachío
Department of Structural Mechanics and Hydraulic Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), 18001 Granada, Spain
Sergio Muñoz
Department of Materials and Transportation Engineering, Escuela Técnica Superior de Ingeniería, University of Seville, 41092 Seville, Spain
Jose Luis Escalona Franco
Department of Materials and Transportation Engineering, Escuela Técnica Superior de Ingeniería, University of Seville, 41092 Seville, Spain
Antonio J. Guillén
Department of Management, Complutense University of Madrid, 28040 Madrid, Spain
Adolfo Crespo Marquez
Department of Industrial Management, Escuela Técnica Superior de Ingeniería, University of Seville, 41092 Seville, Spain
Sergio Cantero-Chinchilla
School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1TR, UK
This paper presents a deep learning approach for predicting rail corrugation based on on-board 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.
