Listening to the Machines
In manufacturing, a single machine failure can halt an entire production line, costing thousands of dollars per minute. We implemented an IoT Predictive Maintenance system that collects real-time data (vibration, temperature, acoustic) from industrial equipment. By analyzing these signals with AI, we can detect the subtle signs of wear and tear weeks before a breakdown occurs.
The Challenge
The Solution
Key Features
Anomaly Detection
Flag abnormal behavior that deviates from the healthy baseline.
- Vibration spikes
- Temperature drift
- Acoustic signatures
RUL Prediction
Estimate the 'Remaining Useful Life' of critical components.
- Bearing wear
- Motor efficiency
- Oil quality
Automated Work Orders
Trigger a maintenance ticket automatically when risk exceeds a threshold.
- CMMS integration
- Technician alerts
- Parts ordering
Technology Stack
Project Timeline & Results
Sensor Deployment
Connected 50 critical assets to the cloud.
Selected industrial-grade sensors. Installed gateways to bridge the gap between the factory floor (OT) and the cloud (IT). Verified data connectivity and quality.
Baseline Modeling
Established a 'digital fingerprint' for healthy machine operation.
Collected 30 days of baseline data. Trained the unsupervised learning model to understand normal operating ranges. Tuned sensitivity to avoid false alarms from normal load variations.
Live Monitoring
Predicted a motor bearing failure 2 weeks in advance.
Went live with the monitoring dashboard. The system successfully flagged a developing fault in a conveyor motor, allowing maintenance to be scheduled during a planned shift change, saving $50k in potential downtime.
Related Services
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