Anomaly detector

Purpose

Anomaly detector analyzes real-time sensor data using machine learning to identify abnormal operational behavior compared to historical baselines.

Primary users

Anomaly detector is intended for external, client-facing users who need to monitor operational and sensor data for abnormal behavior.

Where it fits (process/stage/trigger)

Anomaly detector fits into operational monitoring processes where client operational and sensor data is reviewed in real time and compared against historical baselines to identify potential deviations.

Key capabilities / workflow

Anomaly detector analyzes incoming sensor data, checks whether the data can be used, compares operational behavior against historical baselines, and delivers detected anomalies when abnormal behavior is identified.

Inputs

The input for Anomaly detector is client operational and sensor data.

Outputs / Deliverables

The output of Anomaly detector is detected anomalies in operational behavior.

Value

Anomaly detector helps identify abnormal operational behavior from real-time sensor data, supporting faster visibility into deviations from historical operational patterns.