A review of the concept, applications, risks and control strategies for digital twin

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.