Definition
Anomaly detection automatically identifies when a signal or pattern deviates from an asset's established normal behavior, without necessarily explaining why or forecasting a specific future failure. It flags deviations for a technician to investigate, distinct from predictive analytics, which estimates a specific future outcome.
Key facts
- Flags deviation from expected behavior; it does not by itself explain the cause or predict a specific failure date.
- Requires a defined baseline of "normal" behavior, often learned from historical data for the same asset or asset class.
- A flagged anomaly still needs investigation and confirmation before it becomes a maintenance decision.
- Distinct from predictive analytics, which forecasts a specific future outcome rather than only flagging present deviation.
How does Anomaly detection work in maintenance?
Anomaly detection compares current signal patterns (vibration, current, temperature, process values) against a learned or defined baseline of normal operation and raises a flag when the deviation exceeds expected variation, without asserting a specific cause.
What does Anomaly detection require?
Condition monitoring is the ongoing data collection anomaly detection analyzes; anomaly detection is one analytical step applied to that data. Predictive analytics goes further, estimating a specific future outcome such as time-to-failure: anomaly detection only flags that current behavior differs from the norm, leaving cause and forecast for further analysis.
FAQ
Frequently asked questions
How are deviations from expected asset behavior detected?
Anomaly detection compares current signals against a learned or defined baseline of normal behavior and flags deviations for investigation, without by itself explaining the cause or predicting a specific failure date.
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