Anomaly detection
Using AI to flag readings that deviate from normal patterns, often before a failure occurs.
Anomaly detection uses statistical or machine-learning methods to learn what normal looks like for each signal, then flags readings that deviate. Applied to infrastructure, it surfaces emerging problems earlier than fixed thresholds can.
Self-learning thresholds also reduce alert noise, so operators see fewer false alarms and more signal.
In ProDCIM
ProDCIM applies AI anomaly detection, forecasting and self-learning thresholds to catch problems before they become outages and to cut alert noise.
AI Management System