This is all possible because of IoT sensors, which gather masses of data and artificial intelligence technologies, including machine learning and data analytics. These provide the insights and predictions based on this data.
From the production line to investor funds
Automobile manufacturers have created the digital twin car, which comprises the car’s exterior, mechanics, electrics and software. Diverse scenarios can be tested from design to production to find issues, failures and solutions before the new model is built.
However, as the technology evolves, these data-rich virtual models are being used in many other situations. For instance, urban planners can get a bird’s eye overview of the city or drill down to the most accurate detail at street or building level, to improve infrastructure, plan new constructions or run disaster scenarios and take measures to prevent floods and train first responders. Banks and investment services use digital twins to run simulated cyber attacks in order to improve the security of customer funds, while surgeons enhance patient safety and care by practicing on digital twin patients or body parts before operating.
The need for standards
e-tech caught up with Dr Sha Wei, Convenor of the IEC and ISO Advisory Group carrying out standardization work for digital twins. This group is part of the joint technical committee which develops standards for information technology (ISO/IEC JTC 1). Wei works for the China Electronics Standardization Institute, which is a mirror committee for JTC 1, as well as in standardization of smart manufacturing and artificial intelligence.
How will standards contribute to this technology?
Digital twins are an important enabling technology and driving force of digitization, which is reshaping the world in multiple scales, such as for buildings, factories, automobiles or entire cities.
“As digital twins are implemented, there are several standardization requirements for aspects of terminology, reference architecture and semantic interoperability. To start with, in the literature and reports on digital twins, stakeholders, such as developers, engineers and users, refer to the technology as digital representation, digital mapping, CPS and administration shell. Digital world, as one of the essential components of digital twins, is also called virtual world, virtual space and cyber space. This is why terminology and definitions are always the first standard to be developed for new technologies, so that everyone is on the same page.”
In order to describe the current application of digital twins in smart manufacturing, smart cities or smart energy and provide a general understanding of how digital twins work, a general reference architecture is necessary.
“We need to develop a general reference architecture, which would cover components, such as data connected from the physical world, models stored in the digital world, a communication interface between the digital and physical worlds, services, as well as their reciprocal processes. Also, data is the most important element for realizing system optimization using digital twins. In practice, because data is normally collected from different sources and unstructured, the consistency of the digital and physical worlds is not easy to guarantee. Therefore, a standard defining semantic interoperability between the digital and physical worlds, is vital for the implementation of digital twins.”
What is the main area of focus?
Digital twins are being applied in many areas, including smart manufacturing, smart cities, smart energy, smart farming, smart transport, smart buildings, smart healthcare, etc. Smart manufacturing is one of the most active areas for these applications. As summarized in the paper Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues, by authors Lu, Yuqian & Liu, Chao & Wang, Kevin & Huang, Huiyue & Xu, Xun, GE has developed a digital twin platform PREDIX that can better understand and predict asset performance. SIEMENS uses a digital twin to cover smart operations during the complete process of product design, production and operation. ABB emphasizes the digital twin’s effects on enabling data-driven decision makings. Microsoft has geared up its digital twin product portfolio, providing a ubiquitous IoT platform for modelling and analyzing the interactions between people, spaces and devices. Initiatives from these tech leaders have significantly pushed the boundaries of digital twins for engineering applications.
“We are focusing on a general IT framework standard for digital twins, which can be applied to different areas, but a series of new horizontal standards, such as terminology, reference architecture and semantic interoperability needs to be formulated to provide general understandings for different stakeholders and different application areas. I’d like to cooperate with experts from relevant standards development organizations to generate harmonized digital twins.”
More about IEC standardization activities
IEC works together with ISO on the development of international standards for information and communication technologies. The Joint Technical Committee (ISO/IEC JTC 1) covers many areas including AI, automatic identification and data capture techniques, biometrics, cloud computing, data usage, IoT, IT for learning, education and training, virtual reality and quantum computing. Find out more. Digital twins is also a focus area of IEC Technical Committee 65, which develops international standards for industrial process measurement, control and automation. For example, TC 65 Working Group 16 is working on a framework that specifies model elements and rules for creating and managing digital representations of production systems.