@misc{10481/94016,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10481/94016},
abstract = {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.},
organization = {Project Geminhi (Digital model
for Intelligent Maintenance based on Hybrid prognostics models) (Grant US-1381456, founded by
Junta de Andalucía, Andalucía FEDER 2014–2020)},
organization = {AMADIT Project (PID2022-137748OB-C32),
funded by MCIN/AEI/10.13039/501100011033/FEDER, EU},
publisher = {MDPI},
keywords = {Rail corrugation},
keywords = {Deep learning},
keywords = {Convolutional neural networks},
title = {Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements},
doi = {10.3390/s24144627},
author = {Haghbin, Masoud and Chiachío Ruano, Juan and Muñoz, Sergio and Escalona Franco, Jose Luis and Guillén, Antonio J. and Crespo Marquez, Adolfo and Cantero Chinchilla, Sergio},
}