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Your search keyword '"Guillén, Antonio J."' showing total 8 results
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8 results on '"Guillén, Antonio J."'

1. On the Definition of Requirements for a Digital Twin. A Case Study of Rolling Stock Assets

Author

Crespo Márquez, Adolfo, Leturiondo, Urko, Marcos, José A., Guillén, Antonio J., Candón, Eduardo, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Crespo Márquez, Adolfo, editor, Gómez Fernández, Juan Francisco, editor, González-Prida Díaz, Vicente, editor, and Amadi-Echendu, Joe, editor

Published
2023
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2. Review of Asset Digitalization Models in the Context of Intelligent Asset Management and Maintenance

Author

Alonso, Pilar Jiménez, Guillén, Antonio J., Gómez, Juan Fco., Candón, Eduardo, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Crespo Márquez, Adolfo, editor, Gómez Fernández, Juan Francisco, editor, González-Prida Díaz, Vicente, editor, and Amadi-Echendu, Joe, editor

Published
2023
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5. Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle's Acceleration Measurements.

Author

Haghbin, Masoud, Chiachío, Juan, Muñoz, Sergio, Escalona Franco, Jose Luis, Guillén, Antonio J., Crespo Marquez, Adolfo, and Cantero-Chinchilla, Sergio

Subjects
CONVOLUTIONAL neural networks, ROLLING stock, ACCELERATION measurements, DEEP learning, ACCELERATION (Mechanics), VELOCITY measurements
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. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Standards as Reference to Build a PHM-Based Solution

Author

Guillén, Antonio J., González-Prida, Vicente, Gómez, Juan Fco, Crespo, Adolfo, Koskinen, Kari T., editor, Kortelainen, Helena, editor, Aaltonen, Jussi, editor, Uusitalo, Teuvo, editor, Komonen, Kari, editor, Mathew, Joseph, editor, and Laitinen, Jouko, editor

Published
2016
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