Building Virtual Sensors using Machine Learning

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Virtual sensing techniques are useful to provide economical and feasible alternatives to impractical or costly physical measurements.

A virtual sensing system can be implemented by creating a machine learning model. This model extract information from other measurements and give an estimate of the quantity of interest.

This approach promises cost savings because otherwise physical, costly, tasks are performed only virtualy.

Contents:

  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Conclusions.

Objectives

Virtual sensing tries to model our target variable so there is no need to measure it physically.

The challenge is to get the right variables for our model to predict the quantity of interest.

Benefits

It allows to obtain meassures by using a machine learning model, reducing costs and work hours, as these meassures must usually be obtained in a laboratory.

REDUCE COSTS

SAVE TIME

Approach

The way to virtual sensing is to create an approximation model that takes in some input variables related to out target and estimates the latter.

Neural networks can model the correct values of the target variable so we don't need to take physical meassures.

That allows to save costs and time on meassure's obtention.

Conclusions

Virtual sensing makes companies save money and time since meassure taking becomes a software issue, which can improve planning and decission making.

Neural Designer uses machine learning to build predictive models that represent a broad range of variables associated with virtual sensing.

Related solutions:

Related examples:

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