Electric induction motors are the type of motor most commonly operated in industry, and for this reason technologies that predict faults and reduce the corrective maintenance are of great interest. In this context, this paper presents a predictive maintenance tool of electric motors using the concepts of Digital Twin (DT) and Industrial Internet of Things (IIoT). The proposed system is innovative, as it monitors the motor current and temperature by means of sensors and a low-cost acquisition module, and these measurements are sent via Wi-Fi to a database. The concept of DT was leveraged by providing the measurements as inputs to a high-fidelity strongly-coupled model of the monitored monitor, using the Finite Element Method (FEM). The results obtained are satisfactory, because the sensors used presented acceptable errors that do not interfere with the reliability of the results. The computer simulation showed relative errors below 4% in the conductivity analysis and 10% in the temperature analysis. In addition, the simulation allows verifying the internal temperature of the motor, its resistive losses, and the intensity of the magnetic flux at each pole. It is worth pointing out that the internal analysis performed is only possible due to the combination of IIoT and computer simulations. Therefore, they allow a better diagnosis of the motor’s operational status and also a time estimate for the next maintenance service, thus being ideal for the industrial sector.

Photovoltaic systems are among the renewable energy sources with the greatest global impact, driven by technologies that enable real-time monitoring, predictive maintenance, and intelligent integration with the electricity grid. In this context, this paper presents the design and implementation of an embedded Internet of Things (IoT) system to monitor temperature and humidity in photovoltaic systems in the Amazon region. The system was implemented in a photovoltaic solar plant located at the Federal University of Pará and used to monitor parameters such as local humidity and temperature, with the latter being considered at three strategic points: the surface of the photovoltaic module exposed to direct solar radiation, the shaded area of the module, and the ambient temperature. The results obtained showed good performance from the embedded system, with emphasis on the ease of remotely updating the embedded system’s code and centralized visualization of the monitored data in an IoT middleware. The device proved to be resistant to the adverse climatic conditions of the Amazon, allowing the operators and managers of the photovoltaic plant to monitor and visualize the measured variables and to draw up preventive and corrective maintenance strategies. In this way, the embedded system designed and implemented is a valuable tool for the photovoltaic plant’s operators and managers, promoting greater energy efficiency, reducing operating costs and increasing the useful life of the modules. It also contributes to the Sustainable Development Goals (SDGs), such as SDG 7 (Clean and affordable energy) and SDG 13 (Climate action).

The Motor Monitoring and Analysis System (SMAM) was developed as a tool that can help with the predictive maintenance of electric motors through Digital Twin (DT) and the Industrial Internet of Things (IIoT). To do this, SMAM has an embedded system made up of the ESP32 microcontroller and temperature, voltage and current sensors, in which the measurements taken are stored in the Firebase web database. It is worth noting that the sensors are calibrated using the least squares and linear regression mathematical methods, as well as using the ET-5062 energy analyzer as a reference to ensure that the measurement is reliable. In addition, strong coupling simulations are carried out in the Finite Element Method Magnetics (FEMM) software, which makes it possible to visualize the magnetic flux and the internal temperature of the motor by entering the measured current data. In this way, SMAM is a promising resource for the manufacturing environment.