Intelligent Bearing System Health Monitoring for Smart Manufacturing Conveyors

Bearing systems are critical components in smart manufacturing conveyors, where unplanned failures can lead to significant downtime and costs. Traditional condition-based maintenance (CBM) methods lack scalability in dynamic, sensor-rich environments. This paper presents a Bearing Edge Analytics and Condition Observation Network (BEACON) framework real-time monitoring and fault detection. BEACON employs advanced sensor networks, unsupervised and supervised learning models, and explainable AI techniques to enhance interpretability. Experimental validation on a Festo conveyor system demonstrated that BEACON archieved 95% accuracy in vibration-based fault detection, with Long Short-Term Memory (LSTM) models achieving the highest anomaly detection performance (94%). The results highlight the potential of BEACON for scalable, real-time predictive maintenance in Industry 4.0 applications.