Predictive Maintenance
Predictive maintenance uses sensor data and machine learning to detect equipment degradation before failure, enabling scheduled interventions instead of emergency repairs.
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, unnecessary maintenance.
- Predictive — fix it when sensors indicate degradation. 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 deviates, the system alerts maintenance teams — typically 5-24 days before failure.
Proven Results
VLTHRLAB validated predictive maintenance on 8 public datasets: AUC up to 1.000, zero false alarms on cross-dataset transfer, 86% cost reduction, 72% downtime reduction in digital twin simulation.