Abstract
Predictive maintenance of industrial equipment has become a critical aspect in the Industry 4.0. This paper shows the design, implementation and testing of an Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system will be the basis for detection of operating anomalies and a future predictive maintenance system. The system has been designed using low-cost hardware components (wireless multi-sensor modules and single-board computer as gateway), open-source software and a free version of an IoT analytics service in the cloud, where all the relevant information is stored. The module gathers real-time data about the vibrations and temperature of an electric motor. Vibration analysis in the temporal and frequency domains was carried out. Furthermore, analysis in the frequency domain was carried out both in the module and in the gateway to compare their capabilities. This approach is also the springboard to take advantage of edge and fog computing as a complement to cloud computing. The prototype has been tested in a laboratory and in an industrial dairy plant.