Digital twins are digital representations of physical assets, with most people using them in the context of the Internet of Things (IoT). Digital twins effectively connect sensors to an application to visualize the state of the asset, often represented by a 3D model, and can be used for a variety of purposes and intents.
To clarify with some examples:
- Nowadays, aircraft engines can be monitored live, in-flight to identify safety risks and upcoming maintenance so that the maintenance crew is ready when the aircraft lands.
- An F1 car is monitored in real-time with many sensors so that the pit crew can change settings, instruct the driver to slow down or change his behavior on the track if needed.
- A digital twin of a robot arm can be used by a surgeon to perform a remote operation.
Of course, getting real-time sensor data into, for example, a control room and getting assets into 3D models isn’t all that new. Factories have been doing this for many years and some have even been combining it. However, it has been difficult getting this information beyond the control room.
How would you connect remote off-site assets? How do you get the relevant information to the field service engineer or the mechanic on the floor for that matter? The rise of IoT, cloud and sheer computing power enables us to enter a new area of combining real-time sensor data and 3D models—that’s what it is meant with the digital twin.
Taking an individual asset, representing it in a digital 3D model with real-time sensor data, complete with maintenance data, provides a lot of potential for many businesses.
There are two opportunities in this approach to take it a step further; extending our view beyond the traditional sensor and maintenance data and recognizing the asset as a part of a larger system.