What is predictive maintenance?
Predictive maintenance uses sensor data and machine learning to detect equipment degradation before failure, enabling scheduled interventions instead of emergency repairs. It reduces costs by 86%, downtime by 72%, and maintenance costs by 6x.
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 that degradation is occurring — typically 5-24 days before failure.
Proven Results
VLTHRLAB validated predictive maintenance on 8 public datasets with AUC up to 1.000, zero false alarms on cross-dataset transfer, and 86% cost reduction in digital twin simulation.
Evidence
VERIFIED
86% cost reduction
Source: vlthrlab.app/research/mad-system.html
Source: vlthrlab.app/research/mad-system.html
VERIFIED
72% downtime reduction
Source: vlthrlab.app/research/mad-system.html
Source: vlthrlab.app/research/mad-system.html
VERIFIED
5-24 days early detection
Source: vlthrlab.app/research/mad-system.html
Source: vlthrlab.app/research/mad-system.html