Predictive Maintenance (PdM)
Last updated 2026.02.13예지보전PdM설비관리머신러닝센서데이터Predictive MaintenanceEquipment ManagementMachine Learning
Definition
Predictive Maintenance (PdM) is a maintenance strategy that monitors equipment condition in real-time to predict the optimal maintenance timing before failure occurs. Unlike routine preventive maintenance, it performs maintenance only when warranted based on actual equipment condition data, achieving both cost savings and improved uptime.
Application in Manufacturing
AI-Driven PdM Systems
Manufacturing sites implement predictive maintenance by combining sensor data with AI technologies:
- Vibration, Temperature, Current Sensors: Real-time monitoring of rotating equipment, motors, and pumps
- Machine Learning Models: Early anomaly detection after learning normal operational patterns
- Remaining Useful Life (RUL) Prediction: Proactive spare parts planning and inventory optimization
Real-World Cases
- CNC Machines: 70% reduction in unexpected failures through spindle bearing vibration analysis
- Injection Molding: Prevention of mold damage via hydraulic system pressure pattern analysis
- Conveyor Systems: Transition to planned maintenance through motor current signature analysis
Key Benefits
Advantages over Traditional Preventive Maintenance:
- ⏱️ 30-50% reduction in unnecessary maintenance tasks
- 📈 20-30% improvement in equipment availability
- 💰 25-40% decrease in maintenance costs
- 🎯 Minimized production downtime from unexpected failures
As a core application of manufacturing AI, PdM becomes even more sophisticated when integrated with digital twin technology.