The Problem
- CNC machine breakdowns halt production lines without warning
- Spindle failures and tool breakage cause scrap and rework
- Preventive maintenance schedules often service machines that don't need it
- No visibility into which machines are degrading
- Emergency repairs cost 3-5x more than planned maintenance
Modern Approach
- Vibration sensors detect spindle and bearing degradation early
- Current monitoring identifies motor stress before failure
- AI analyzes trends and predicts failures 2-4 weeks ahead
- Maintenance dashboard prioritizes work by risk and impact
- Integration with spare parts system for proactive ordering
Frequently Asked Questions
How does predictive maintenance prevent CNC failures?
Sensors continuously monitor vibration, temperature, and motor current. AI analyzes these patterns and detects early signs of spindle wear, bearing degradation, or motor stress weeks before failure occurs.
What equipment benefits most from predictive maintenance?
High-value machines where downtime is costly: CNC machining centers, grinding machines, and automated lines. Equipment with long lead time for replacement parts also benefits significantly.
How long before failures can predictive systems detect issues?
Typical systems provide 2-4 weeks advance warning for most failure modes. This gives time to order parts and schedule repairs during planned downtime.
How long does it take to install predictive maintenance?
Sensor installation takes 1-2 days per machine. The AI system starts learning patterns immediately and provides predictions after 2-4 weeks of baseline data.
What is the ROI of predictive maintenance?
ROI comes from avoided downtime, reduced emergency repair costs, and extended equipment life. Most facilities see 10-20x return on monitoring investment through prevented failures.