A Digital twin is a digital replica of a living or non-living physical entity. Digital twin refers to a digital replica of potential and actual 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. 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, synchronize 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).
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 of which it may be a part. 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 to as “cyberobjects”, or “digital avatars”. The digital twin is also a component of cyber-physical systems.
Digital twins were anticipated by David Gelernter’s 1991 book Mirror Worlds. It is widely acknowledged in both industry and academic publications that Michael Grieves of Florida Institute of Technology first applied the digital twin concept in manufacturing. The concept and model of the digital twin was publicly introduced in 2002 by Grieves, then of the University of Michigan, at a Society of Manufacturing Engineers conference in Troy, Michigan. Grieves proposed the digital twin as the conceptual model underlying product lifecycle management (PLM).
The concept, which had a few different names, was subsequently called the “digital twin” by John Vickers of NASA in a 2010 Roadmap Report. The digital twin concept consists of three distinct parts: the physical product, the digital/virtual product, and connections between the two products. The connections between the physical product and the digital/virtual product is data that flows from the physical product to the digital/virtual product and information that is available from the digital/virtual product to the physical environment.
The concept was divided into types later. The types are the digital twin prototype (DTP), the digital twin instance (DTI), and the digital twin aggregate (DTA). The DTP consists of the designs, analyses, and processes to realize a physical product. The DTP exists before there is a physical product. The DTI is the digital twin of each individual instance of the product once it is manufactured. The DTA is the aggregation of DTIs whose data and information can be used for interrogation about the physical product, prognostics, and learning. The specific information contained in the digital twins is driven by use cases. The digital twin is a logical construct, meaning that the actual data and information may be contained in other applications.
A digital twin in the workplace is often considered part of robotic process automation (RPA) and, per industry-analyst firm Gartner, is part of the broader and emerging “hyperautomation” category.
An example of how digital twins are used to optimize machines is with the maintenance of power generation equipment such as power generation turbines, jet engines and locomotives. In enterprise architecture (EA), architects create EA blueprints as digital twin for the organization.
Another example of digital twins is the use of 3D modeling to create digital companions for the physical objects. It can be used to view the status of the actual physical object, which provides a way to project physical objects into the digital world. For example, when sensors collect data from a connected device, the sensor data can be used to update a “digital twin” copy of the device’s state in real time. The term “device shadow” is also used for the concept of a digital twin. The digital twin is meant to be an up-to-date and accurate copy of the physical object’s properties and states, including shape, position, gesture, status and motion.
A digital twin also can be used for monitoring, diagnostics and prognostics to optimize asset performance and utilization. In this field, sensory data can be combined with historical data, human expertise and fleet and simulation learning to improve the outcome of prognostics. Therefore, complex prognostics and intelligent maintenance system platforms can use digital twins in finding the root cause of issues and improve productivity. Digital twins of autonomous vehicles and their sensor suite embedded in a traffic and environment simulation have also been proposed as a means to overcome the significant development, testing and validation challenges for the automotive application, in particular when the related algorithms are based on artificial intelligence approaches that require extensive training data and validation data sets.
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