What is Digital Twin?
As assets and systems become more complicated, the way we develop, manage, and maintain machines for a connected world needs to evolve. We need tools to meet the new realities of software-driven products fueled by digital disruption. Enter the digital twin. It’s a technological leap ‘through the looking glass’ into the very heart of physical assets. Digital twins allow us to see into what is happening, or what may happen, with physical assets now and far into the future.
Let’s start with the basics: what is a digital twin?
Here is a straightforward definition of digital twin:
“A digital twin is a virtual representation of an object, asset or system. It spans the item’s lifecycle, should be updated from real-time data, and uses simulation, machine learning, and reasoning to aid in decision-making.”
Simply put, it means creating a detailed virtual model that is the counterpart (or twin) of a physical asset. The ‘asset’ could be a car, a building, a bridge, or a jet engine. Connected sensors on the physical asset collect data and map it onto the virtual model. Anyone looking at the digital twin can now see crucial information about how the physical thing is doing out there in the real world.
Digital twins allow us to understand the present and predict the future and are a vital tool to help engineers and operators understand how products are performing and how they will perform in the future. Analysis of the data from the connected sensors, combined with other sources of information, allows us to make these predictions.
With this information, organizations can learn faster. They can also break down old boundaries surrounding product innovation, complex lifecycles, and value creation.
Digital twins help manufacturers and engineers accomplish a great deal, like:
- Visualizing products in use, by real users, in real-time
- Building a digital thread, connecting disparate systems, and promoting traceability
- Refining assumptions with predictive analytics
- Troubleshooting far away equipment
- Managing complexities and linkage within systems-of-systems
Let’s look at some of these in more detail.
Use cases for digital twin: an engineer’s point of view
Let’s look at an example of digital twins in action. And since the primary users of digital twins are engineers, let’s use their perspective.
An engineer’s job is to design and test products – whether cars, jet engines, tunnels, or household items – with their complete lifecycle in view. They need to ensure that the product they are designing is suitable for the purpose, can cope with wear and tear, and responds well to its working environment.
Creating real-world scenarios, virtually
An engineer testing a car braking system, for example, would run a computer simulation to understand how the system would perform in various real-world scenarios. This method has the advantage of being a lot quicker and cheaper than building multiple physical cars to test. But there are still some shortcomings.
First, computer simulations like the one described above are limited to current real-world events and environments. They can’t predict how the car will react to future scenarios and changing circumstances. Second, modern braking systems are more than mechanics and electrics…they’re also comprised of millions of lines of code.
Enter digital twin and IoT. A digital twin uses data from connected sensors to tell the complete story of an asset through its lifecycle, from testing to use in the real world. We can measure specific indicators of asset health and performance with IoT data, like temperature and humidity, for example. By incorporating this data into the virtual model or the digital twin, engineers have a complete view of how the car is performing through real-time feedback from the vehicle.
Part Two: What is the Value of Using Digital Twin?