TY - GEN
T1 - A Review of the Concept, Applications, Risks and Control Strategies for Digital Twin
AU - Farid, Farnaz
AU - Bello, Abubakar
AU - Jahan, Nusrat
AU - Sultana, Razia
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/2/24
Y1 - 2024/2/24
N2 - The concept and application of digital twin has been advancing and intersecting various fields. The Internet of Things (IoT), Cyber-Physical Systems (CPS), cloud computing, and big data are examples of emerging technologies being incorporated into Industry 4.0. Effective monitoring and management of physical systems are possible through the utilization of machine learning and deep learning methodologies for the analysis of gathered data. Along with the development of IoT, a number of CPS: smart grids, smart transportation, smart manufacturing, and smart cities, also adopt IoT and data analytic technologies to improve their performance and operations. Yet, several risks exist when directly modifying or updating the live system. As a result, the production of a digital clone of an actual physical system, often known as a “Digital Twin” (DT), has now become an approach to address this issue. This study aims to conduct a review on how digital twins are utilized to improve the efficiency of intelligent automation across various business sectors. The study provides an understanding of the concept and discusses the evolution and development of digital twins. The key technologies that enable digital twins are examined, and the risks and challenges associated with digital twins are analyzed together with potential control strategies.
AB - The concept and application of digital twin has been advancing and intersecting various fields. The Internet of Things (IoT), Cyber-Physical Systems (CPS), cloud computing, and big data are examples of emerging technologies being incorporated into Industry 4.0. Effective monitoring and management of physical systems are possible through the utilization of machine learning and deep learning methodologies for the analysis of gathered data. Along with the development of IoT, a number of CPS: smart grids, smart transportation, smart manufacturing, and smart cities, also adopt IoT and data analytic technologies to improve their performance and operations. Yet, several risks exist when directly modifying or updating the live system. As a result, the production of a digital clone of an actual physical system, often known as a “Digital Twin” (DT), has now become an approach to address this issue. This study aims to conduct a review on how digital twins are utilized to improve the efficiency of intelligent automation across various business sectors. The study provides an understanding of the concept and discusses the evolution and development of digital twins. The key technologies that enable digital twins are examined, and the risks and challenges associated with digital twins are analyzed together with potential control strategies.
KW - Digital Twin
KW - IOT
KW - CPS Digital Clone
KW - Digital Twin Risks
KW - Digital Twin Security Controls
UR - http://www.scopus.com/inward/record.url?scp=85197347809&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85197347809&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-54820-8_21
DO - 10.1007/978-3-031-54820-8_21
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85197347809
SN - 9783031548192
T3 - Lecture Notes in Networks and Systems
SP - 264
EP - 282
BT - Data Analytics in System Engineering - Proceedings of 7th Computational Methods in Systems and Software 2023, Vol. 4
A2 - Silhavy, Radek
A2 - Silhavy, Petr
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Computational Methods in Systems and Software, CoMeSySo 2023
Y2 - 12 April 2023 through 13 April 2023
ER -
