CLOUD-BASED, MACHINE LEARNING ASSISTED ANALYSIS OF RESIDUAL CURRENTS WITH A WARNING SYSTEM FOR ANOMALIES

e.Guard advance uses the recorded residual current data together with machine learning methods to predict the future condition/status of the installation.

In addition to the functions of level THREE, e.Guard advance also offers the following key features:
› Machine learning of individual residual current patterns during operation
› Detection and notification of anomalies across all frequency components up to 100 kHz
› Triggers an alarm if the threshold value is exceeded and if anomalies are detected in the residual current, e.g. creeping insulation faults, deterioration of EMC filters