The term 'Digital twin' refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things device operates and lives throughout its life cycle. The term was coined in 2002 by Michael Grieves at the University of Michigan.
Definitions of digital twin technology used in prior research emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart. Secondly, this connection is established by generating real time data using sensors. The concept of the digital twin can be compared to other concepts such as cross-reality environments or co-spaces and mirror models, which aim to, by and large, synchronise part of the physical world (e.g., an object or place) with its cyber representation (which can be an abstraction of some aspects of the physical world). Worthy of mention is David Gelernter's book on Mirror Models. Digital twins integrate internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system, learns from itself, using sensor data that conveys various aspects of its operating condition; from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; from other similar fleets of machines; and from the larger systems and environment in which it may be a part of. A digital twin also integrates historical data from past machine usage to factor into its digital model. In various industrial sectors, twins are being used to optimize the operation and maintenance of physical assets, systems and manufacturing processes. They are a formative technology for the Industrial Internet of things, where physical objects can live and interact with other machines and people virtually. In the context of the Internet of things, they are also referred as "cyberobjects", or "digital avatars". The digital twin is also a component of the Cyber-physical system concept.
Internet of things
As an exact digital replica of something in the physical world, digital twins are made using Internet of Things (IoT) sensors that gather data from the physical world and send it to machines to reconstruct. By creating a digital twin, insights about how to improve operations, increase efficiency or discover an issue are all possible before it happens to whatever it's duplicating in the real world. The lessons learned from the digital twin can then be applied to the original system with much less risk and a lot more return on investment.
Digital twin technology was included on Gartner's Top 10 Strategic Technology Trends for 2017 and 2018. Gartner predicted there would be 21 billion connected sensors by 2020, making digital twins possible for billions of things.
Similar to the benefits in manufacturing, digital twins can revolutionize healthcare operations as well as patient care. A digital twin of a patient or organs allows surgeons and health professionals to practice procedures in a simulated environment rather than on a real patient. Sensors the size of bandages can monitor patients and produce digital models that can be monitored by AI and used to improve care.
Digital twin technology helps city planners understand and improve the efficiency of energy consumption as well as many applications that can improve life for its citizens.
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