TY  - JOUR
PY  - 2024//
TI  - Predicting rail corrugation based on convolutional neural networks using vehicle's acceleration measurements
JO  - Sensors (Basel)
A1  - Haghbin, Masoud
A1  - Chiachío, Juan
A1  - Munoz, Sergio
A1  - Escalona Franco, Jose Luis
A1  - Guillén, Antonio J.
A1  - Crespo Marquez, Adolfo
A1  - Cantero-Chinchilla, Sergio
SP  - 
EP  - 
VL  - 24
IS  - 14
N2  - 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>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s24144627
ID  - ref1
ER  -