Citation

Haghbin M, Chiachío J, Munoz S, Escalona Franco JL, Guillén AJ, Crespo Marquez A, Cantero-Chinchilla S. Sensors (Basel) 2024; 24(14).

Copyright

(Copyright © 2024, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s24144627

PMID

39066028

PMCID

PMC11281133

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.

Language: en

Keywords

deep learning; convolutional neural networks; Grad-CAM; rail corrugation