A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data
Industry 5.0 has introduced new possibilities for defining key features of the factories of the future. This trend has transformed traditional industrial production by exploiting Digital Twin (DT) models as virtual representations of physical manufacturing assets. In the railway industry, Digital Tw...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10554659/ |
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| author | Giulio Salierno Letizia Leonardi Giacomo Cabri |
| author_facet | Giulio Salierno Letizia Leonardi Giacomo Cabri |
| author_sort | Giulio Salierno |
| collection | DOAJ |
| description | Industry 5.0 has introduced new possibilities for defining key features of the factories of the future. This trend has transformed traditional industrial production by exploiting Digital Twin (DT) models as virtual representations of physical manufacturing assets. In the railway industry, Digital Twin models offer significant benefits by enabling anticipation of developments in rail systems and subsystems, providing insight into the future performance of physical assets, and allowing testing and prototyping solutions prior to implementation. This paper presents our approach for creating a Digital Twin model in the railway domain. We particularly emphasize the critical role of Big Data in supporting decision-making for railway companies and the importance of data in creating virtual representations of physical objects in railway systems. Our results show that the Digital Twin model of railway switch points, based on synthetic data, accurately represents the behavior of physical railway switches in terms of data points. |
| format | Article |
| id | doaj-art-7fffa8de85534c9ba6bee8d65cbc4512 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-7fffa8de85534c9ba6bee8d65cbc45122025-01-24T00:02:50ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01534235910.1109/OJITS.2024.341282010554659A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic DataGiulio Salierno0https://orcid.org/0000-0002-9617-4448Letizia Leonardi1https://orcid.org/0000-0003-4035-8560Giacomo Cabri2https://orcid.org/0000-0002-4942-2453Department of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Engineering “Enzo Ferrari,”, University of Modena and Reggio Emilia, Modena, ItalyDepartment of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena, ItalyIndustry 5.0 has introduced new possibilities for defining key features of the factories of the future. This trend has transformed traditional industrial production by exploiting Digital Twin (DT) models as virtual representations of physical manufacturing assets. In the railway industry, Digital Twin models offer significant benefits by enabling anticipation of developments in rail systems and subsystems, providing insight into the future performance of physical assets, and allowing testing and prototyping solutions prior to implementation. This paper presents our approach for creating a Digital Twin model in the railway domain. We particularly emphasize the critical role of Big Data in supporting decision-making for railway companies and the importance of data in creating virtual representations of physical objects in railway systems. Our results show that the Digital Twin model of railway switch points, based on synthetic data, accurately represents the behavior of physical railway switches in terms of data points.https://ieeexplore.ieee.org/document/10554659/Big datadigital twinmachine learningsynthetic datarailway industryartificial intelligence |
| spellingShingle | Giulio Salierno Letizia Leonardi Giacomo Cabri A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data IEEE Open Journal of Intelligent Transportation Systems Big data digital twin machine learning synthetic data railway industry artificial intelligence |
| title | A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data |
| title_full | A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data |
| title_fullStr | A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data |
| title_full_unstemmed | A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data |
| title_short | A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data |
| title_sort | big data architecture for digital twin creation of railway signals based on synthetic data |
| topic | Big data digital twin machine learning synthetic data railway industry artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10554659/ |
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