Preventing Unexpected Loom Breakdowns with Predictive Maintenance

Predictive maintenance uses sensors to monitor loom health continuously. AI analyzes vibration, temperature, and motor data to predict failures 2-4 weeks before they happen, letting you schedule repairs during planned downtime instead of emergency stops.

Sensors attached to looms detect early warning signs like unusual vibrations, temperature spikes, or motor strain. When readings drift from normal patterns, the system alerts maintenance with specific diagnosis before the loom fails.

Common causes include bearing failures, belt wear, motor issues, and mechanical fatigue. Most breakdowns show warning signs days or weeks before failure - predictive systems catch these early.

Unplanned downtime costs Rs 5,000-15,000 per hour per loom in lost production alone. Add emergency repair costs, overtime, rush shipping for parts, and quality issues from degraded operation.

Basic sensor installation takes 1-2 days per loom. The system starts learning normal patterns immediately. Accurate predictions begin after 2-4 weeks of baseline data collection.