IoT sensor-based machine learning system for anticipating equipment failures and optimizing maintenance schedules
A large manufacturing facility experienced frequent unexpected equipment breakdowns that caused costly production delays and emergency repairs. Their reactive maintenance approach resulted in significant downtime, high repair costs, and lost productivity. The company needed a proactive solution to predict equipment failures before they occurred.
We deployed IoT sensors across critical machinery to collect real-time data on vibration, temperature, pressure, and operational parameters. Machine learning algorithms analyzed this sensor data along with maintenance history to identify patterns indicating impending failures. The system provided maintenance recommendations with predicted failure timelines and confidence levels.
The predictive maintenance system achieved 87% accuracy in predicting equipment failures 2-4 weeks in advance, enabling proactive maintenance scheduling. Unplanned downtime was reduced by 42%, significantly improving production efficiency. The company realized $2.3M in annual savings through reduced emergency repairs, optimized maintenance schedules, and improved equipment lifespan.