Part 3

How Digital Twins work

Machine learning and predictive modeling can give managers new insights and decision-making power.

Digital Twin Technology can be complex, so looking at a real-world example like Intangles’ Digital Twin platform for trucks can illustrate how the technology works in action.

The first step is the creation of a “base” Digital Twin, using OEM information on the make and model of the vehicle and historical data gathered from other, similar vehicles and/or their Digital Twins.

The twin exists in the cloud—a vast global network of connected servers—and it doesn’t really look like a truck, but rather a series of data (visualized into charts) that capture relevant information about the real-world vehicle. The Digital Twin gets that data by being “connected” to its physical twin via a telematics device installed in the real-world truck; with the Intangles platform, data from more than 450 sensors are continuously transmitted to the Digital Twin.

 

As more and more information comes in from the real truck, the Digital Twin becomes more and more complete, reflecting its physical counterpart with greater accuracy. Over time, it becomes much more than a copy of the make and model of a truck; instead, it becomes a copy of that particular vehicle, reflecting its mileage, its wear and tear, its driver’s behaviour and a host of other variables—even road conditions, weather and traffic. Because it’s such a good copy and is observable even thousands of miles away from the real thing, the Digital Twin can give fleet managers insights into vehicle and driver performance in real time, at a level that they could never achieve with a physical truck.

 

Yet the Digital Twin can do far more than enable better diagnostics. It can also help fleet managers predict future issues. How? That’s where AI comes in. Through so-called machine learning—a branch of artificial intelligence that explores ways to make machines learn the way humans do—the truck’s Digital Twin gets smarter all the time. Following a set of instructions known as an algorithm, the Digital Twin “learns” from its own physical twin, as well as from historical data and data from other similar Digital Twins in the cloud.

 

The Digital Twin can also be used as a virtual test truck, through another advanced AI functionality called predictive modeling. Using algorithms, physical-twin and historical data are analyzed mathematically to forecast future outcomes—for instance, whether and when a particular part is likely to break down and will require maintenance, or how much fuel will be in the physical trunk’s tank at each stage of the trip. Effectively, the Digital Twin can help fleet managers identify issues before they occur and even make recommendations to address them, potentially reducing repair costs and keeping vehicles on the road.

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