Self-sustainable IoT Wireless Sensor Node for Predictive Maintenance on Electric Motors

Unexpected equipment failure is expensive and potentially hazardous for workers and users. Periodic inspections and maintenance at predefined intervals aim to limit unplanned production downtime, costly replacement of parts and safety concerns. On the other side, predictive maintenance techniques can monitor equipment as it operates, anticipating deterioration and incoming breakages, enabling just-in-time services at reduced operational costs. This paper presents a deploy and forget predictive maintenance sensor node designed explicitly for industrial electric motors. It is targeted for AC mono and three-phase asynchronous motors and generators, measuring vibrations, environmental noise, temperature, and the external magnetic field. The proposed sensor achieves self-sustainability by exploiting a 4x4 cm thermal source for 72 s with a ∆T of 20 °C, and it features short-long wireless data transfer respectively over WiFi and the cellular NB-IoT network. We tested the prototype on different electric motors, form 4 kW to 110 kW, reporting here its capability to detect anomalies using a vibration spectral analysis.