Predictive Maintenance for Smart Manufacturing

Unplanned equipment downtime was costing the manufacturing plant over $10M annually. Reactive maintenance was inefficient, and scheduled maintenance often replaced parts prematurely or too late.

We implemented an IoT sensor network across critical equipment and built ML models to predict equipment failures 2-4 weeks in advance. The system analyzes vibration, temperature, pressure, and operational data to identify anomalies and predict maintenance needs.