The digital twin is the virtual representation of a physical object or system across its life-cycle. It uses real-time data and other sources to enable learning, reasoning, and dynamically recalibrating for improved decision making.
In plain English, this just means creating a highly complex virtual model that is the exact counterpart (or twin) of a physical thing. The ‘thing’ could be a car, a tunnel, a bridge, or even a jet engine. Connected sensors on the physical asset collect data that can be mapped 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.
What this means is that a digital twin is a vital tool to help engineers understand not only how products are performing, but 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 more, faster, and break down old boundaries surrounding product innovation, complex life-cycles, 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
An engineer’s job is to design and test products – whether cars, jet engines, tunnels or household items – with their complete life-cycle in view. In other words, they need to ensure that the product they are designing is suitable for purpose, can cope with wear and tear, and will respond well to the environment in which it will be used.
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 thousands of lines of code.
This is where digital twin and the IoT come in. A digital twin uses data from connected sensors to tell the story of an asset all the way through its life-cycle. From testing to use in the real world.
With IoT data, we can measure specific indicators of asset health and performance, like temperature and humidity, for example. By incorporating this data into the virtual model, or the digital twin, engineers have a full view into how the car is performing, through real-time feedback from the vehicle itself.
The value of digital twin: understanding product performance
Digital twins give manufacturers and businesses an unprecedented view into how their products are performing. A digital twin can help identify potential faults, troubleshoot from afar, and ultimately, improve customer satisfaction. It also helps with product differentiation, product quality, and add-on services, too.
If you can see how customers are using your product after they’ve bought it, you can gain a wealth of insights. That means you can use the data to (if warranted), safely eliminate unwanted products, functionality, or components, saving time and money.
IBM’s work with digital twin
The International Business Machines Corporation (IBM) has been doing a lot of work with digital twin technologies. Just this year, they announced new lab services for Maximo, bringing Augmented Reality (AR) into asset management. The IBM lab service ‘turns on’ many visual and voice (Natural Language Processing) features for your workforce. This enables you to see your assets in a new dimension and get instant access to critical data. You can then feed those insights back to others using an AR helmet with voice/video in the visor. This makes ‘interacting’ the next evolution of working.
