Unsupervised Predictive Maintenance on Industrial Electric Motors Based on Self-Sustainable IoT Wireless Sensor Nodes

The integration of IoT technologies and data-driven Machine Learning (ML) algorithms is revolutionizing the industrial sector, mainly through predictive maintenance and Industry 4.0. However, most of the solutions used today are based on fully-supervised ML approaches, necessitating large datasets and detailed knowledge of the system under monitoring. This work proposes an innovative, fully unsupervised, fault detection and predictive maintenance system for industrial electric motors based on an self-sustainable IoT sensor node and an online clustering algorithm. The system acquires real-time sparse data through an ultra-low power wireless sensor node, using a DBSCAN-based approach to identify operational conditions and anomalies without the need for pre-training or dataset collection. Experimental field tests on different three-phase motors, based on a 6-month continuous trial, have demonstrated the method’s effectiveness, highlighting the algorithm’s ability to dynamically adapt to new operating conditions correctly, discriminating among 7 classes. Our system reached a 97% accuracy in correctly discriminating anomalies and 74% in automatically identifying operative conditions on a previously unknown electric motor. The results confirm the potential of the proposed approach to improve industrial maintenance reliability and efficiency, reducing costs and unexpected downtime.