What is predictive maintenance?
Predictive maintenance uses sensor data and machine learning to detect equipment wear before failure, enabling planned repairs instead of emergency fixes. It reduces costs by 86%, downtime by 72.9%, and maintenance costs by 4.87x.
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 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 that wear is occurring — typically 5-24 days before failure.
Proven Results
VLTHRLAB tested 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
72.9% 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