AI-Powered Predictive Maintenance
Built machine learning models to predict equipment failures, reducing unplanned downtime for a major industrial client.
The Challenge
A major industrial manufacturer was losing millions annually to unplanned equipment failures. Their maintenance approach was purely reactive — they fixed machines only after they broke down. Sensor data existed across thousands of IoT devices but was unused, sitting in disconnected silos with no analytics pipeline.
Our Solution
We built an end-to-end ML pipeline that ingests real-time sensor data, detects anomaly patterns, and predicts failures 72 hours in advance. The system was trained on 3 years of historical failure data and deployed as a real-time dashboard accessible to floor managers and maintenance teams. Alerts are pushed via SMS and email when intervention is needed.
Results
Measurable Impact
40%
Reduction in unplanned downtime
72h
Advance failure prediction
$2.1M
Annual savings
3,000+
IoT sensors connected
Tech Stack
Technologies Used
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