Can machine learning replace an expensive physical sensor? Let’s see whether the information you need can be inferred from the data you already collect.
In many industrial systems, an important variable may be expensive, difficult, slow, or impractical to measure continuously.
But it may be possible to infer that variable from data you’re already collecting.
A virtual sensor uses mathematical and machine-learning models to estimate an unobserved variable from other available measurements:
Existing sensor data → ML model → real-time estimate
For example, a virtual sensor could estimate:
product quality or composition between laboratory measurements
equipment wear or condition
temperature or pressure at an inaccessible location
emissions or concentrations that are expensive to measure continuously
a failed or drifting sensor from other correlated measurements
The model can also provide prediction uncertainty, so the system knows when its estimate is reliable and when a physical measurement may still be required.
Genius Mathematics Consultants can develop custom virtual-sensor models using your existing historical data and integrate them into operational systems.
Is there something in your process you wish you could measure continuously?
It may already be hidden in the data you’re collecting.
If you’re interest in having us build a virtual sensor for your business, don’t hesitate to Contact Us.