Canary Edge ships with a powerful generic model that works out of the box. For production workloads, setting a baseline for each machine fine-tunes a dedicated predictor that learns the specific patterns of your equipment.
When to Set a Baseline
- Your time series has unique seasonal patterns
- The generic model produces too many false positives
- You need maximum sensitivity for critical equipment
- You want regime classification (HEALTHY/ACTIVE/TRANSITION/SHOCK)
How It Works
- Send normal operating data via
POST /v1/baseline with a machine_id
- Canary Edge computes energy statistics and trains a lightweight predictor (~462K params) in seconds
- Future detection calls with that
machine_id use the fine-tuned model automatically
- Detection accuracy typically improves from ~82% (generic) to 97%+ (fine-tuned)
Setting a Baseline
Only send energy scores from normal operating data for baseline creation. Including anomalous data will degrade detection accuracy.
Checking a Baseline
Response:
Requirements
- Minimum 100 energy values for a reliable baseline
- Data should represent at least one full operational cycle
- All data should be from normal, healthy operation
Machine Status
Once a baseline is set, check machine status:
Deleting a Machine
To remove a baseline and revert to the generic detector:
