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Predictive Maintenance System
An IoT and AI-based system that predicts equipment failures before they occur, helping manufacturing clients reduce downtime and maintenance costs by up to 40%.
Overview
IoT-driven platform to forecast industrial equipment failures using sensor data and ML.
Key Features
- Real-time anomaly detection
- Customizable maintenance schedules
- Root cause analysis dashboards
Challenges
- Noisy sensor data from legacy manufacturing equipment
- Balancing false positives (unnecessary downtime) vs. false negatives (missed failures)
- On-premise deployment for factories with limited connectivity
Strategic Approach
- Deployed edge computing devices to preprocess sensor data locally
- Built ensemble ML models (Random Forest + LSTM) for time-series forecasting
- Collaborated with domain experts to label 2+ years of historical failure data
- Designed a "digital twin" simulator to test predictions under edge cases
Technologies
- IoT: Raspberry Pi + Modbus protocol
- ML: PyTorch, Scikit-learn
- Visualization: Grafana, Tableau
Impact
- Reduced unplanned downtime by 40% for automotive manufacturers
- Saved $850K/year in maintenance costs per factory
- Extended equipment lifespan by 15% on average
Future Roadmap
- Add AR-guided repair workflows for technicians
- Federated learning for cross-factory model improvements
Ready to Transform Your Maintenance Operations?
Contact us to learn how our predictive maintenance solution can optimize your manufacturing processes.
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