Anomaly Detection
Anomaly detection uses statistics or machine learning to flag when equipment or process behaviour deviates from its normal pattern, catching problems that fixed alarm limits miss. In industry it gives early warning of developing faults and efficiency drift.
Rather than waiting for a value to cross a fixed threshold, anomaly detection learns what 'normal' looks like across many variables and operating states, then flags meaningful deviations. This catches subtle, multivariable problems — a slow drift in a turbine's behaviour, an emerging exchanger fouling trend — well before a single-sensor alarm would trip.
Related terms
Condition Monitoring · Digital Twin · Predictive Maintenance (PdM)
Related guides
Predictive maintenance: a practical guide
What predictive maintenance is, how it differs from preventive maintenance, which techniques fit which assets, and how to start without boiling the ocean.
Heat exchanger fouling: causes and prevention
Why exchangers foul, what it costs in energy and throughput, and how to predict and manage cleaning instead of reacting to it.