@article{ref1,
title="Predicting rail corrugation based on convolutional neural networks using vehicle's acceleration measurements",
journal="Sensors (Basel)",
year="2024",
author="Haghbin, Masoud and Chiachío, Juan and Munoz, Sergio and Escalona Franco, Jose Luis and Guillén, Antonio J. and Crespo Marquez, Adolfo and Cantero-Chinchilla, Sergio",
volume="24",
number="14",
pages="-",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s24144627",
url="http://dx.doi.org/10.3390/s24144627"
}