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Active and Passive Data


As reminder, a Digital Twin is a virtual model of an existing object, process or service.
To run this model efficiently, the Digital Twin needs to be fed with data.

In the Large Industry, these data can be divided in two categories: Passive or Active.

Digital Twin

For an equipment, Passive Data gather all the information related to physical characteristics of the asset itself.
For example: the actuator position, the rotation speed, the cast temperature, the input voltage etc.

The Active Data are also captured by the equipment but go beyond the asset to describe the process.
For example: fluid viscosity in a valve, flow in a pump, gas temperature in a pipeline, charge rate of a motor.


Between Active and Passive Data, interests and Use Cases may differ.

By nature, Passive Data are asset centric, leading to Product Life Management considerations.
Predictive maintenance is one of the most common use case issue from Digital Twin and is exclusively based on Passive Data.
In fact, the digital model learns every running patterns of an equipment and can detect at early stage any deviations from the normal behavior. Once the deviation catched, maintenance team can proceed.

On the other side, Active Data are related to the process and create value at an higher stage than the asset.
Performance monitoring is an intuitive use case born from the collection of Active Data and its data processing.
Indeed, based on historical data, the Digital Twin simulates multiple scenarios to find the optimal performance point and optimize operations of a plant/factory.

Many more applications can be listed from the use of an equipment data, but it’s important to bear in mind these data can also create value beyond the asset itself.

To note, when Passive Data are often directly sourced by sensors linked to the equipment, Active Data may require some extrapolation to be obtained.

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