Anomaly Detection
The automated identification of equipment behavior that deviates from its learned normal pattern.
Anomaly detection is the technique of flagging sensor readings or operational patterns that deviate from an asset's established baseline. Rather than checking values against fixed thresholds, modern anomaly detection learns what normal looks like for a specific machine — its vibration signature at typical load, its temperature curve across a shift, its energy draw by hour — and raises an alert when live data drifts outside that envelope.
The advantage over static thresholds is context. A pump drawing 40 amps may be normal at full load and alarming at idle; a fixed limit cannot tell the difference, but a model trained on that pump's history can. This reduces false alarms, which matters because alert precision determines whether a monitoring program is trusted or muted.
In practice, anomaly detection is the front end of a workflow, not an end in itself. A detected anomaly should carry context — the trend, the affected asset, a probable cause — and feed directly into work order creation so a human can inspect and confirm. Detections that end at a dashboard rarely change outcomes.
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