Off late, I have been sharing some views on digital technology and how that is disrupting old business models and transforming human lives around the world. The man-machine interactions and near real-time availability of large data sets are opening new vistas in the industrial world. And, artificial intelligence, machine learning, and smart things promise an intelligent future.
The concept of ‘digital twins’ was introduced by Dr. Michael Grieves of University of Michigan while he was working in collaboration with John Vickers of NASA. Industry giant GE is taking this concept to new heights. Gartner predicts ‘digital twin’ among top 10 strategic technology trends for 2017.
What’s Digital Twin? Digital twins refer to computerized companions of physical assets that can be used for various purposes. The concept of the digital twin requires three elements: the physical product in real space, its digital twin in virtual space and the information that links the two. Digital twins use data from Sensors installed on physical objects to represent their near real-time status, working condition or position.
One example of digital twins can be the use of 3D modeling to create a digital companion for the physical object. 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 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.
In another context, Digital twin can be also used for monitoring, diagnostics, and prognostics. In this field, sensory data is sufficient for building digital twins. These models help to improve the outcome of prognostics by using and archiving historical information of physical assets and perform a comparison between fleets of geographically distributed machines.
Therefore, complex prognostics and Intelligent Maintenance System platforms can leverage the use of digital twins in finding the root cause of issues and improve productivity. Digital twins of physical assets combined with digital representations of facilities and environments as well as people, businesses and processes will enable an increasingly detailed digital representation of the real world for simulation, analysis and control.
While the digital twin was initially introduced in manufacturing industries, the applications will quickly grow in all areas of human endeavor; healthcare, retail, banking and finance to name a few.
Government and business organizations who adapt non-linear strategies would get benefited immensely with the new approach.
Key question. What’s the readiness to adapt digital twin in your organization?
*Picture and video courtesy: Internet sources and GE.
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