Digital twin refers to a digital replica of physical assets (physical twin), processes and systems 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.
Digital twins integrate artificial intelligence, machine learning and software analytics with data 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.
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.
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.
Examples of industry applications:
- Aircraft engines
- Wind turbines
- Large structures e.g. offshore platforms, offshore vessels etc.
- HVAC control systems
- Finite element method
- Internet of things
- Industry 4.0
- Health and usage monitoring systems
- Integrated vehicle health management
- Digital workplace
- Digital business
- "Minds + Machines: Meet A Digital Twin". Youtube. GE Digital. Retrieved 26 July 2017.
- "Introduction to Digital Twin: Simple, but detailed". Youtube. IBM Watson Internet of Things. Retrieved 27 June 2017.
- "Digital twin to enable asset optimization". Smart Industry. Retrieved 26 July 2017.
- "What Are Digital Twins And Why Will They Be Integral To The Internet Of Things?". ARC. Retrieved 26 July 2017.
- "Shaping the Future of the IoT". YouTube. PTC. Retrieved 22 September 2015.
- "On Track For The Future – The Siemens Digital Twin Show". YouTube. Siemens. Retrieved 22 September 2015.
- "'Digital twins' could make decisions for us within 5 years, John Smart says". news.com.au. Retrieved 22 September 2015.
- "Digital Twin for MRO". LinkedIn Pulse. Transition Technologies. Retrieved 25 November 2015.
- Marr, Bernard. "What Is Digital Twin Technology – And Why Is It So Important?". Forbes. Forbes. Retrieved 7 March 2017.
- Grieves, Michael. "Digital Twin: Manufacturing Excellence through Virtual Factory Replication" (PDF). Florida Institute of Technology. Retrieved 24 March 2017.
- "GE Doubles Down On 'Digital Twins' For Business Knowledge". InformationWeek. Retrieved 26 July 2017.
- "Device Shadows for AWS IoT – AWS IoT". docs.aws.amazon.com.
- "Digital Twin for SLM". YouTube. Transition Technologies. Retrieved 26 November 2015.
- "Digital Twin for Machine Monitoring". Youtube. IMS Center. Retrieved 6 March 2016.
- "Digital Twin Wind Turbine". Youtube. IMS Center. Retrieved 6 March 2016.
- "Wind Turbine Digital Twin". IMS Center. IMS Center.
- "GE Oil & Gas 2017 Annual Meeting: 'Digital: Exploring what's possible' with Colin Parris". Youtube. GE Oil & Gas. Retrieved 26 July 2017.
- Lee, Jay; Bagheri, Behrad; Kao, Hung-An (January 2015). "A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems". Manufacturing Letters. 3: 18–23. doi:10.1016/j.mfglet.2014.12.001.
- Lee, Jay; Lapira, Edzel; Bagheri, Behrad; Kao, Hung-an (October 2013). "Recent advances and trends in predictive manufacturing systems in big data environment". Manufacturing Letters. 1 (1): 38–41. doi:10.1016/j.mfglet.2013.09.005.
- Infosys Insights. "The Future For Industrial Services: Digital Twin" (PDF). Retrieved 15 March 2017.
- "The jet engine with 'digital twins'". BBC.com. Retrieved 26 July 2017.
- TWI Ltd. "Lifecycle Engineering Asset Management Through Digital Twin Technology". www.twi-global.com. Retrieved 14 March 2017.
- "HOW TWINNING TECH WILL POWER OUR FUTURE". Retrieved 26 July 2017.
- Bureau Veritas. "Digital technology to transform AIMS". Retrieved 15 March 2017.
- IRS srl (2017-06-01). "Embedded digital twin".
- "Digital Twins elevate industrial asset performance". Control. Retrieved 26 July 2017.
- "Creating a Building's Digital Twin". Wired. Retrieved 1 Feb 2017.