Anomaly Detection

Anomaly detection identifies data points, events or patterns that deviate significantly from expected behaviour, such as unusual machine vibration, a defective product image or suspicious network traffic, often without labelled examples of every fault.

Statistical methods flag values beyond control limits or thresholds derived from the mean and standard deviation. Machine learning methods include isolation forests, one-class support vector machines, clustering and density-based methods, and deep learning approaches such as autoencoders, which learn to reconstruct normal data and flag inputs with high reconstruction error. Time-series methods model expected patterns, including seasonality, and flag deviations from forecasts.

Applications include early warning of equipment faults in predictive maintenance, visual inspection trained mostly on good parts, process monitoring, energy consumption analysis, fraud detection and cyber security monitoring of networks, logs and user behaviour, including OT networks.

Defining normal is the main challenge: operating modes, product changes and seasonal effects can trigger false alarms, while gradual degradation may be learned as normal. Thresholds balance missed detections against alert fatigue. Performance is assessed with precision, recall and time to detection on known incidents, and alerts should provide context to support investigation.

Key points

Flags deviations from expected behaviour in data
Methods include statistical limits, isolation forests and autoencoders
Works where labelled fault examples are scarce
Thresholds must balance missed detections against alert fatigue

Where AiVibe comes in

AiVibe's 24/7 security monitoring uses AI-powered anomaly detection, and predictive maintenance is an AiAmbA use case.

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Anomaly Detection

Key points

Where AiVibe comes in

Related terms

Terms that refer to Anomaly Detection