Unplanned equipment failures were causing significant production downtime and costly emergency repairs. Traditional time-based maintenance schedules resulted in either premature replacements (wasting resources) or unexpected breakdowns (halting production). The facility needed a data-driven approach to predict equipment failures before they occurred.
We designed and deployed an end-to-end IoT sensor network across critical manufacturing equipment, collecting vibration, temperature, pressure, and acoustic data in real-time. Using TensorFlow machine learning models trained on historical failure data, the system predicts equipment degradation patterns and schedules maintenance during planned downtime windows. The AWS IoT infrastructure ensures reliable data collection with MQTT messaging, while the dashboard provides maintenance teams with actionable insights and priority rankings.
30% reduction in unplanned equipment downtime
$2M annual savings in maintenance and production costs
Extended average equipment lifespan by 15%
Maintenance team efficiency improved by 40%
ROI achieved within 8 months of deployment
The technical stack that powered this solution