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Open AccessArticle
Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators
by
Roman Rodriguez-Aguilar
Roman Rodriguez-Aguilar
Prof. Dr. Roman Rodriguez-Aguilar is a professor in the School of Economic and Business Sciences of [...]
Prof. Dr. Roman Rodriguez-Aguilar is a professor in the School of Economic and Business Sciences of the “Universidad Panamericana”, Mexico. His research interests encompass large-scale mathematical optimization, statistical learning, computational intelligence, mathematical finance, health economics, energy economics, digital economics, and digital transformation in organizations. He obtained his PhD at the School of Economics, the National Polytechnic Institute, Mexico. He also obtained his master's degree in Engineering from the National University of Mexico (UNAM), another master's degree in Administration and Public Policy from the Monterrey Institute of Technology and Higher Education,
a postgraduate degree in Applied Statistics from the Research Institute in Applied Mathematics and Systems of the UNAM, and his degree in Economics at the UNAM. Before joining Universidad Panamericana, he worked as a specialist in economics, statistics, finance, and optimization, occupying different management positions in various public and private entities. He has co-authored many research articles in science citation index journals. Professor Rodríguez has supervised many M.Sc. and PhD students. He is a member of the National System of Researchers Level II of CONAHCYT in Mexico.
1,*,
Jose-Antonio Marmolejo-Saucedo
Jose-Antonio Marmolejo-Saucedo 2 and
Utku Köse
Utku Köse 3
1
Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Mexico City 03920, Mexico
2
Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico
3
Faculty of Engineering, Suleyman Demirel University, Isparta 32260, Turkey
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(19), 3124; https://doi.org/10.3390/math12193124 (registering DOI)
Submission received: 6 September 2024
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Revised: 24 September 2024
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Accepted: 30 September 2024
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Published: 6 October 2024
Abstract
The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.
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MDPI and ACS Style
Rodriguez-Aguilar, R.; Marmolejo-Saucedo, J.-A.; Köse, U.
Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators. Mathematics 2024, 12, 3124.
https://doi.org/10.3390/math12193124
AMA Style
Rodriguez-Aguilar R, Marmolejo-Saucedo J-A, Köse U.
Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators. Mathematics. 2024; 12(19):3124.
https://doi.org/10.3390/math12193124
Chicago/Turabian Style
Rodriguez-Aguilar, Roman, Jose-Antonio Marmolejo-Saucedo, and Utku Köse.
2024. "Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators" Mathematics 12, no. 19: 3124.
https://doi.org/10.3390/math12193124
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