Aidan Fuller
Digital Twin: Enabling Technologies, Challenges and Open Research
Fuller, Aidan; Fan, Zhong; Day, Charles; Barlow, Chris
Abstract
Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.
Acceptance Date | May 7, 2020 |
---|---|
Publication Date | May 28, 2020 |
Journal | IEEE Access |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 108952 - 108971 |
DOI | https://doi.org/10.1109/ACCESS.2020.2998358 |
Keywords | applications, Computational modeling, Data analysis, Data models, deep learning, Digital twins, enabling technologies, industrial Internet of Things (IIoT), Internet of Things, Internet of Things (IoT), literature review, machine learning, Manufacturing, |
Publisher URL | https://ieeexplore.ieee.org/document/9103025 |
Files
09103025.pdf
(5.4 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
The role of ‘living laboratories’ in accelerating the energy system decarbonization
(2022)
Journal Article
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
(2022)
Journal Article
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
(2021)
Presentation / Conference
Downloadable Citations
About Keele Repository
Administrator e-mail: research.openaccess@keele.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search