What is Predictive Maintenance?
Unplanned equipment failures cost the process industry billions of dollars annually in lost production, emergency repairs, and safety incidents. Traditional maintenance strategies — run-to-failure or calendar-based schedules — either come too late or waste resources.
Operon's Predictive Maintenance uses machine learning models trained on your actual equipment data to predict failures days or weeks before they occur.
"We caught a bearing failure on our main compressor 12 days before it would have caused a plant trip. That single prediction saved us an estimated M in lost production."
How it works
1. Data Integration
Operon connects to your existing data sources:
- Historians — PI, IP.21, DeltaV, Honeywell PHD
- Vibration monitoring — CSI, Bently Nevada, SKF
- DCS/SCADA — real-time process data
- CMMS — maintenance records, work orders, failure history
No new sensors required — we work with the data you already collect.
2. Equipment-Specific Models
Operon builds models specific to each piece of equipment:
- Rotating equipment — pumps, compressors, turbines, fans
- Heat exchangers — fouling prediction, tube leak detection
- Vessels & columns — corrosion rate prediction, tray damage detection
- Valves — control valve stiction, relief valve testing prediction
Each model learns the normal operating envelope for that specific asset.
3. Failure Prediction
The system continuously monitors for early warning signs:
- Anomaly detection — identifies subtle deviations from normal behavior
- Degradation tracking — monitors gradual equipment deterioration
- Remaining useful life — estimates time until intervention is needed
- Root cause analysis — suggests probable failure modes
4. Action Integration
Predictions connect to your existing workflows:
- Automatic work order generation in your CMMS
- Priority-ranked maintenance recommendations
- Spare parts inventory alerts
- Integration with turnaround planning
Performance
| Metric | Value |
|---|---|
| Failure prediction accuracy | 92%+ |
| Average lead time | 14 days before failure |
| False positive rate | <5% |
| Unplanned downtime reduction | 40-60% |
| Maintenance cost reduction | 25-35% |
Why it matters
By learning from your actual equipment behavior — not generic failure curves — our models predict failures with the specificity your operations team needs.
"We reduced our unplanned downtime by 45% in the first year. The maintenance team went from fighting fires to planning ahead."
Get Started
Predictive Maintenance works best when combined with our P&ID Recognition and Knowledge Graphs. Our on-site engineers handle the full deployment — typically within 4-6 weeks.