Prepare signals
Pin sensor history windows and align them with verified outcomes from the maintenance system.
Industry blueprint
Predictive maintenance
Manufacturing, mining and fleets. Retrain on verified outcomes so false alerts fall instead of climb.
A model trained once on commissioning data degrades as equipment wears; false positives climb month by month. This design retrains on verified maintenance outcomes and measures false alerts before a model goes live. Live alerting stays in your existing monitoring.
Illustrative pipeline
Train a candidate
Run the partner's time-series model on the pinned windows.
Measure false alerts
Score the candidate against the current model on held-out history.
Engineer approves
Go live only after a reliability engineer reviews the comparison.
What the record keeps
The sensor windows, the asset model, the verified outcomes, the candidate and its false-alert rate, and the approver.
Partner brings
- Historian or OPC-UA access
- Asset model and verified outcomes from the CMMS
- Time-series model
Cairn brings
- Pinned windows and quality checks
- False-alert comparison on held-out history
- Bounded retrain loop with approval
- A record of every model that went live
Business value to test
False alerts, detection quality and time to a reviewed investigation.
Keep the decision explicit
A score opens an investigation; it never changes a setpoint. Record the model version and the engineer's conclusion.
Close the feedback loop
Each investigation's outcome becomes a label for the next candidate, which must cut false alerts before it replaces the current model.
Reference design, not a shipped domain pack. Live detection and alerting stay in your SCADA or monitoring vendor; this covers the retraining loop only.
See how failure can trigger a reviewed improvement cycle →Cut false alerts, not investigations.
Talk about your sensor history, maintenance records and review workflow.