Advanced Fault Detection

Schneider-Electric Analytics & AI
Published by Schneider Electric



Advanced Fault Detection analytics is used to detect anomalies in complex and repetitive systems. For instance, it could be used to anticipate failures in pump motors, drives or robotic arms.

It leverages unsupervised machine learning techniques to model the expected normal behavior of a system. Then, it uses this model to detect anomalous behavior in order to anticipate a failure of the system.

Advanced Fault Detection should be used when the system is supposed to be repetitive. It does not require the user to provide a dataset with past failures.

Advanced Fault Detection analytics includes four features:

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

- Calibrate the created model to the optimal false positive rate (finalizeModel).

- Apply the model on new data and detect faults (applyModel).

- Get information on an existing model (getModelInformation).

Advanced Fault Detection

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