Digital Twin Application in Various Sectors

The digital twin is an electronic representation of an actual or intended entity, process, or system (the physical twin) that serves as a digital counterpart that is effectively indistinguishable from the actual entity, system, or process. A digital twin has been envisioned from its inception as a premise for Product Lifecycle Management and as a tool used throughout the entire lifecycle of a physical entity (create, build, operate/support, and dispose). Due to the granularity of information, the digital twin representation depends on the use cases it is created for. It is possible and common for a digital twin to exist before a physical entity does. Simulating and modeling the intended entity’s lifecycle is possible with the use of a digital twin at the creation phase. A digital twin is a prototype to explore the issues of a real entity, it is synchronized with the corresponding real-time scenario to explore the challenges to be handled. The purpose of digital twins is to create digital companions for physical objects using 3D modeling. In this way, physical objects can be projected into the digital world while displaying their status. For instance, when sensors gather data from connected devices, they can be used to update a “digital twin” replica of the device’s state in real time. This technology is alternatively referred to as “device shadow.” The physical properties of objects are an accurate and up-to-date representation of the physical object’s properties such as shape, movement, direction, appearance states, and position. Monitoring, diagnostics, and prognostics can be performed using a digital twin to optimize asset performance. The prognostic outcome can be improved by combining predicted result, sensory data past data, experts data, and reinforcement learning. As a result, digital twins can be used to find the root cause of issues and improve productivity in complex prognostics and intelligent maintenance systems. For the automotive application, digital twins of autonomous vehicles and their sensor suites incorporated into a path of travel have also been suggested as a way to remove the obstacles in development, testing, and validation, especially when artificial intelligence approaches are used to develop the algorithms, which require more training and validation on the data sets. Many industrial use cases can be supported by this technology, such as the manufacturing industry, urban planning and construction industry, healthcare industry, automotive industry, etc.