Anomaly detection

Anomaly detection uses rules, statistics, or AI and Machine Learning to identify behavior that differs from an established or learned pattern.

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Reliability engineers reviewing unusual condition trends in industrial data

Practical guidance

Anomaly detection

An anomaly is not automatically a fault. A change in load, speed, product, maintenance state, sensor health, or environment may also produce unusual data.

A useful industrial anomaly workflow preserves the evidence, compares operating context, and routes the exception to a qualified reviewer before high-consequence action.

Key points

Define normal by operating state
Check the sensor and context first
Treat anomalies as evidence, not diagnoses
Measure false alarms and missed events

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Share the asset, operating concern, data opportunity, or reliability goal. STAH can help shape a focused, human-reviewed next step.

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