Digital Twins are virtual replicas of physical objects, which can be used to monitor and predict the behaviour of the real thing. They are particularly useful for organisations with IoT-enabled machinery such as production lines, factories, energy networks, transport systems, as well as with fleets of discrete objects deployed at multiple remote locations.
Other examples include companies with plant machinery like construction vehicles, robots on a production line, or aero engines.
Digital Twin Visualizations allow holistic, spatially-integrated viewing of all the sensor data for each real object - all its components and their data can be seen in situ. This provides a kind of X-ray vision of the object e.g. parts of an engine may be colour-coded by temperature or speed, so a remote monitoring team can quickly see when parts are overheating, and understand the spatial relationships between each data source.
Additionally, multiple digital twins can be viewed side-by-side even if each real object is in a different geographical location. This allows rapid comparison of a large set of objects, and can help to find emerging issues such as component overheating and failure before they are obvious by other means.
As IoT data proliferates, we are faced with finding ways to understand and derive insight from this vast and rising tide of numerical complexity. Digital Twins provide a compelling way by gathering, processing and presenting vast quantities of machine data in a human-friendly spatial representation.