The Problem: Reactive Maintenance
Unplanned downtime costs manufacturers $260,000 per hour. 70% of maintenance time is wasted on diagnosis ("What is broken?") rather than fixing.
The Solution: Intelligent Maintenance System
We built an end-to-end system combining hardware and software to shift from reactive to predictive maintenance.
1. Edge Device Network
We install rugged IoT sensors (ESP32-based) on critical equipment to monitor vibration, temperature, and current.
- Edge AI: Processes data locally to detect anomalies (e.g., "Motor 3B bearing failure") with 96% accuracy.
- Cost: ~$800 per conveyor setup.
2. Guided Troubleshooting
Instead of a generic alert, the technician receives a specific workflow in the mobile app:
- *Step 1:* Safety Lockout (with photo).
- *Step 2:* Remove Belt (with video guide).
- *Step 3:* Inspect Bearing (interactive 3D diagram).
- *Result:* Diagnosis time reduced from 45 min to 5 min.
3. Predictive Intelligence
The system calculates the Remaining Useful Life (RUL).
- *Scenario:* "Compressor 2A bearing will fail in 18 days."
- *Action:* Schedule repair during planned downtime.
- *Savings:* Avoids an $8,500 emergency repair and $32,000 in production loss.
Development Status
- Current Phase: Sensor Deployment & Data Gathering (Phase 1).
- Target Launch: Q4 2025.
- Investment: $400,000 development budget.
Conclusion
The future of maintenance is predictive, guided, and data-driven. Our application connects the factory floor to the cloud, ensuring machines effectively "heal" themselves before they break.
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