Predictive Maintenance
Predictive maintenance uses sensor data and machine learning to spot equipment wear before failure. This allows planned repairs instead of emergency fixes.
Three Maintenance Strategies
- Run-to-failure — fix it when it breaks. Highest cost, most downtime.
- Preventive — fix it on a schedule regardless of condition. Moderate cost, some unnecessary maintenance.
- Predictive — fix it when sensors show wear. Lowest cost, minimal downtime.
How It Works
Sensors on critical equipment (vibration, temperature, pressure, current) generate continuous data. Machine learning models learn the normal operating pattern. When the pattern changes, the system alerts maintenance teams — usually 5-24 days before failure.
Proven Results
VLTHRLAB tested predictive maintenance on 8 public datasets: AUC up to 1.000, zero false alarms when tested on new datasets, 86% cost reduction, 72% downtime reduction in digital twin simulation.