Predictive Maintenance for Stamping Presses
Implemented condition monitoring system for 80+ hydraulic presses, predicting tool wear and mechanical failures with 92% accuracy, reducing emergency maintenance by 65%.
The Challenge
A large metal stamping facility operated 80 hydraulic presses running 24/7 to meet automotive supplier demands. Unplanned press breakdowns were catastrophic, causing production delays that cascaded through their customer delivery schedule and resulted in significant penalties.
The maintenance team relied on fixed schedules and reactive repairs, unable to predict when critical components would fail. This approach led to 15-20 emergency breakdowns monthly, with some repairs requiring 48+ hours of downtime.
Key Pain Points
- 15-20 emergency breakdowns per month
- 48+ hour average repair time per breakdown
- Unpredictable tool wear causing quality issues
- High emergency repair costs and customer penalties
Our Solution
Hydraulic & Vibration Monitoring
Installed IoT sensors measuring hydraulic pressure, flow rates, temperature, and vibration patterns on all 80 presses for real-time condition monitoring.
Machine Learning Prediction
Trained anomaly detection models to identify degradation patterns and predict failures 5-10 days in advance with 92% accuracy.
Automated Work Orders
Integrated system automatically generates prioritized maintenance work orders, enabling preventive repairs during scheduled maintenance windows.
Results & Impact
Emergency Maintenance Reduction
Maintenance Cost Reduction
Production Uptime Increase
Additional Benefits
Ready to Eliminate Unplanned Downtime?
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