What We Build
Vibration sensors and current monitors attached to each loom. A central dashboard that shows machine health in real-time. Alert system that notifies maintenance before failures happen.
The system tracks bearing wear, belt tension, motor load, and other failure indicators. Historical data shows which machines need attention and when.
How It Works
Sensors collect data continuously while looms run. AI models compare current readings against normal patterns. When something drifts, maintenance gets alerted with specific diagnosis. You schedule repairs during planned downtime instead of emergency stops.
Who This Is For
Weaving units with 20+ looms where unplanned downtime costs real money. If you're spending more on emergency repairs than planned maintenance, this system changes that equation.
Predictive Maintenance for Textile Looms – Summary for Mill Owners
Why It Matters
- A single loom breakdown can stop an entire line, costing lakhs per hour in Coimbatore, Tirupur, Surat, and Bengaluru mills.
- Traditional time-based maintenance either:
- Services too early → wasted maintenance budget
- Services too late → breakdowns and production loss
- Predictive maintenance optimises based on actual machine condition, not fixed calendars.
How Boolean & Beyond’s Predictive Maintenance Works
- Continuous Monitoring with IoT Sensors on:
- Bearings (vibration sensors)
- Motors (current/energy monitors)
- Critical joints (temperature sensors)
- Mechanical assemblies (acoustic sensors)
- AI & Machine Learning analyse real-time sensor streams against patterns of normal operation to detect early signs of:
- Bearing wear
- Belt tension changes
- Motor load imbalance
- Lubrication degradation
- Prediction Window: AI forecasts failures 2–4 weeks in advance, enabling planned maintenance instead of emergency stops.
- Actionable Alerts include:
- Which component is at risk
- Type of degradation
- Estimated time to failure
- So technicians arrive with the right parts and tools.
Implementation Approach
Phase 1: Sensor Installation & Baseline (Weeks 1–4)
- Joint survey with your maintenance team in Coimbatore or Bengaluru.
- Identify failure-prone parts: bearings, belts, motors, shuttle mechanisms.
- Install non-invasive retrofit sensors that:
- Do not affect loom operation
- Do not void OEM warranties
- Work even on older Sulzer, Toyota, Picanol, Dornier, and Indian-made looms.
- Collect baseline data under:
- Different fabric types
- Different loom speeds
- Varying ambient conditions
Phase 2: AI Model Training (Weeks 5–8)
- Use:
- Baseline sensor data
- Your historical maintenance and breakdown records (where available)
- Train component-level failure prediction models.
- Validate predictions with your senior maintenance technicians so that:
- Early warnings match real-world experience
- Models are tuned to minimise both missed failures and false alarms.
Phase 3: Deployment & Maintenance Integration (Weeks 9–12)
- Go live with:
- Real-time monitoring dashboards
- Mobile alerts for maintenance teams
- Integrate with your operations so that when AI predicts, for example, a bearing failure in 3 weeks:
- System checks spare part availability automatically
- Suggests optimal maintenance windows based on your production schedule
- Can trigger work orders in your ERP/CMMS.
Expected Results & ROI
Mills using Boolean & Beyond’s loom predictive maintenance typically see:
- Unplanned downtime: 40–60% fewer unexpected breakdowns
- Maintenance cost: 20–30% reduction in total maintenance spend
- Equipment life: 15–25% longer component life
- Spare parts inventory: 30–40% reduction in inventory value while maintaining availability
- Production throughput: 10–15% increase in effective loom utilisation
- Payback period: ROI in 6–10 months for most mills in Coimbatore, Tirupur, and Bengaluru.
Integration with Existing Mill Systems
- Loom Compatibility: Works with any age/manufacturer:
- Sulzer, Toyota, Picanol, Dornier, Indian-made looms, and mixed fleets.
- ERP Integration:
- Automatic work order creation