Virtual Sensor Fault Detection

Schneider-Electric Analytics & AI
Published by Schneider Electric



Virtual Sensor Fault Detection (VSFD) analytics is used to detect faults in a variable of importance. For instance, to detect overheating in electrical assets or to detect thermal anomalies in HVAC process.

It leverages supervised machine learning techniques to learn the relationship between the target variable, a set of heterogenous available variables (inputs), and to identify drift between the predicted value of the target and its actual variable.

VSFD should be used when the target variable is supposed to have an underlying pattern and when measurements of the target variable are available.

Virtual Sensor Fault Detection analytics includes four features:

- Learn the model from past input and target data (createModel).

- Apply the model on new data to estimate the target and detect (applyModelAndDetectFaults).

- Update an existing model with actual data (updateModel).

- Get information on an existing model (getModelInformation).

Virtual Sensor Fault Detection

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