Condition monitoring of industrial equipment has become a critical aspect in Industry 4.0. This paper shows the design, implementation and testing of a low-cost Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system can be used to detect operating anomalies and paves the way for building predictive maintenance models. The system is built using low-cost hardware components (wireless multi-sensor modules and single-board computers as gateways), open-source software and open cloud services, where all the relevant information is stored. The module collects real-time vibration data from electric motors. Vibration analyses in the temporal and frequency domains were carried out in both modules and gateways to compare their capabilities. This approach is also a springboard to using edge/fog computing to save cloud resources. A system prototype has been tested in the laboratory and in an industrial dairy plant. The results show that the proposed system can be used for continuous monitoring of any rotatory machine with similar accuracy to professional monitoring devices but at a significantly lower cost.

工业设备的状态监测已成为工业 4.0 的一个关键方面。本文展示了低成本工业物联网 (IIoT) 系统的设计、实施和测试，该系统旨在实时监控电动机。该系统可用于检测操作异常并为构建预测性维护模型铺平道路。该系统使用低成本硬件组件（无线多传感器模块和单板计算机作为网关）、开源软件和开放云服务构建，所有相关信息都存储在其中。该模块从电动机收集实时振动数据。在模块和网关中都进行了时域和频域的振动分析，以比较它们的能力。这种方法也是使用边缘/雾计算来节省云资源的跳板。系统原型已在实验室和工业乳品厂进行了测试。结果表明，所提出的系统可用于连续监测任何旋转机器，其精度与专业监测设备相似，但成本显着降低。