Overview:
Predictive maintenance uses AI to analyze data from equipment sensors (vibration, temperature, acoustics, electrical currents) to predict when a machine is likely to fail. This enables manufacturers to perform maintenance only when necessary—before a costly breakdown occurs.
Key Technologies:
- Machine learning (especially time-series forecasting)
- IoT sensor integration
- Anomaly detection algorithms
- Edge computing for real-time analysis
Benefits:
- Up to 30% reduction in maintenance costs
- 70% reduction in machine breakdowns
- Improved asset longevity and uptime
- Real-time health dashboards for machines
Real-World Example:
GE uses AI in its manufacturing plants to monitor jet engine components. Their system predicts part failures weeks in advance, saving millions in unplanned outages.
Angle for Your Blog:
Highlight how predictive maintenance shifts maintenance from reactive to proactive, and tie it into how digital twins are also evolving this space.
