CONTENT PREVIEW
Equipment

Digital twins show promise

29 March 2019
SITA is exploring the possible benefits of digital twins for New York La Guardia, as pictured here on 26 March during PTE 2019 in London. Source: IHS Markit/Patrick Allen

Its time has not quite arrived, but the potential of digital twinning is clear

Digital twins are not a new concept but with greater capacity for data-processing, the rise of artificial intelligence (AI), and the maturing Internet of Things (IoT), they are a technology that is ready for take off.

In essence, digital twins are a sophisticated and malleable modelling system that reflects the features of real-world objects and applies digital technologies – including historic and current events data processed by AI – to give users a better understanding of, and better control over, an object (such as an aircraft, runway, taxiway layout, or airport terminal). These digital twins can also be extended to interact with each other, exchanging data and insights for a ‘big picture’ view.

The possibilities are nearly limitless and exciting, and the technology is at an advanced phase of gestation but it is not quite ready for birth.

Beyond the hype

Technology research and advisory firm Gartner put digital twins in the ‘Peak of Expectations’ phase of its Hype Cycle, and in the “top five emerging technology trends that will blur the lines between human and machine”.

Gartner places digital twins as part of the broader trend of digitalised ecosystems: smart spaces that support equipment self-reporting, systems automation, predictive models, and new human–machine interfaces.

“Digitalised ecosystem technologies are making their way to the Hype Cycle fast,” said Mike Walker, research vice-president at Gartner. “Blockchain and IoT platforms have crossed the peak by now, and we believe that they will reach maturity in the next five to 10 years, with digital twins and knowledge graphs on their heels.”

The advance of digital twins will be driven in part by the maturity of IoT infrastructure, which would supply the necessary data for AI to process and inform the digital models of real-world objects.

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