Predictive Maintenance System for European Manufacturing Company

A manufacturing company experienced €1.2M annual losses from unplanned equipment downtime. Reactive maintenance approach led to production delays, missed deadlines, and emergency repair costs. They had sensor data but no way to predict failures before they occurred.

We developed custom machine learning models trained on 2 years of sensor data (temperature, vibration, pressure, acoustic) to predict equipment failures 48-72 hours in advance. The system integrates with their maintenance scheduling software and sends alerts to technicians. Models are continuously retrained with new data for improved accuracy.