Manufacturing IoT Monitoring System
Reducing downtime through predictive maintenance and real-time equipment monitoring
The Challenge
A mid-sized manufacturing facility was experiencing frequent, unexpected equipment failures that resulted in costly production downtime. Their maintenance approach was purely reactive, and they had no way to predict when machines would fail. Each hour of downtime cost approximately $15,000 in lost production.
They needed a solution to monitor equipment health in real-time, predict failures before they happened, and transition from reactive to predictive maintenance to minimize unplanned downtime.
Our Solution
IoT Sensor Network
Deployed 200+ sensors across 45 machines monitoring temperature, vibration, pressure, and power consumption
Real-Time Monitoring Dashboard
Built React-based dashboard displaying live equipment status, alerts, and historical trends
Predictive Maintenance ML Models
Developed TensorFlow models to predict equipment failures 24-48 hours in advance
Automated Alert System
Implemented SMS/email alerts for anomaly detection and predicted failures
Technology Stack
Results & Impact
- 73% reduction in unplanned downtime through predictive maintenance
- $180K annual savings from reduced downtime and optimized maintenance schedules
- 98.5% equipment uptime achieved, up from 87% before implementation
- ML model accuracy of 89% in predicting failures 24-48 hours in advance
"This system fundamentally changed how we approach maintenance. Instead of reacting to breakdowns, we can now schedule maintenance during planned downtime. The predictive models have been remarkably accurate, and the ROI was evident within six months. Our production efficiency has never been higher."