02 February 2020
8 min
Predictive Maintenance Playbook: From Sensor Logs to Work Orders
Predictive maintenance works when it integrates with maintenance operations and feedback loops.
Predictive MaintenanceMLIndustry
Predictive maintenance works when it integrates with maintenance operations and feedback loops.
Framework
- Define failure events and label policy
- Start with anomaly detection if labels are weak
- Move to supervised models when event history is reliable
- Deploy with clear alert thresholds + human review path
- Monitor drift and false alarm rate
Pitfalls
- Failure labels are inconsistent across teams
- Alarms do not map to real failures
- No feedback loop from technicians → model never improves
Portfolio deliverables
- Alert policy (thresholds + actions)
- Maintenance feedback form and storage
- Monitoring dashboard for false alarms and drift
Good practice
Ship a baseline + monitoring first. Then iterate with evidence.
FAQ
Should we use deep learning for sensors?
Not necessarily. Start with baselines; many cases work with simple models + good features.
What’s the best first metric?
False alarm rate + downtime avoided. Accuracy alone is misleading.
Want to go deeper?
Ask for a brochure, a syllabus, or a live walkthrough of our training projects and delivery standards.
Contact us