Failure Prediction and Predictive Maintenance for Heavy Rotating Machines
Utilizing IoT sensors mounted on industrial machines, our project captures critical telemetry data such as vibrations, rotational speed, and temperature. This data is transmitted to an analytics server for AI-driven analysis, aiming to assess machine health and predict potential failures. Our AI models effectively forecast machine failures 20 to 42 hours in advance, enabling timely corrective actions that prevent downtime. Deployed across over 2000 machines, our solution has significantly cut operational costs and achieved a return on investment in just 11 months, enhancing maintenance efficiency and reliability.