Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements
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.
Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements
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.
Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements
Haghbin, Masoud (Autor:in) / Chiachío, Juan (Autor:in) / Muñoz, Sergio (Autor:in) / Franco, Jose Luis Escalona (Autor:in) / Guillén, Antonio J. (Autor:in) / Marquez, Adolfo Crespo (Autor:in) / Cantero-Chinchilla, Sergio (Autor:in)
17.07.2024
Haghbin , M , Chiachío , J , Muñoz , S , Franco , J L E , Guillén , A J , Marquez , A C & Cantero-Chinchilla , S 2024 , ' Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements ' , Sensors , vol. 24 , no. 14 , 4627 , pp. 1-18 . https://doi.org/10.3390/s24144627
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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