Time-Series Anomaly Detection
Spot unusual patterns in machine data instantly.
Anomaly detection continuously monitors sensor and process time series and flags deviations from normal behavior. Unlike fixed thresholds, it also catches subtle, emerging issues, enabling fast intervention before damage occurs.
- Earlier detection of creeping issues
- Fewer false alarms than fixed thresholds
- Faster root-cause analysis via flagged time windows
incident response time
damage from late detection
payback period
early indicators detected
Derived from the cost of past incidents and the time earlier detection would have saved.
- 01
Signal selection
Determine relevant time series from equipment and processes.
- 02
Learn normal behavior
Build a baseline from historical data without incidents.
- 03
Anomaly models
Combine statistical and ML methods for deviation detection.
- 04
Threshold tuning
Tune false-alarm rates together with operations staff.
- 05
Live monitoring
Integrate dashboards and alerts into the control room.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Process time series
Continuous measurements from equipment.
Historian archives
Long-term data for baseline modeling.
Alarm logs
Past alarms to validate new models.
Operating states
Context such as startup/shutdown or product changeover.
Maintenance events
Cross-referencing anomalies with interventions.
Ambient conditions
External factors such as ambient temperature.
Shift supervisors
Receives early, reliable warning signals.
Maintenance manager
Can narrow down root causes faster.
Process engineers
Gains insight into previously undetected patterns.
IT/OT leads
Operates a system with clear data lineage.
Earlier detection of creeping issues
Fewer false alarms than fixed thresholds
Faster root-cause analysis via flagged time windows
Higher operator trust in warning systems
Transferable across many asset types without rebuilding
With a solid data foundation this use case gets faster, cheaper and far more stable.
Real-time processing of large-scale time-series data
Unified alarm management across all assets
Historical data instantly usable for new models
Traceable model decisions for the control room
Predictive maintenance
Anomalies provide training signals for failure models.
Energy optimization
Unusual consumption is flagged automatically.
Digital twin
Anomaly patterns improve simulation accuracy.