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.

Important industrial applications are based on magnetohydrodynamics (MHD), which concerns the flow of electrically conducting fluids immersed in external magnetic fields. Using the Finite Volume Method, we performed a 3D numerical study of the MHD flow of a conducting fluid in a circular duct. The flow considered was laminar and fully developed. Along the initial section of the duct, there were magnets placed around the duct producing magnetic fields in the radial direction. Two arrangements of magnetic field orientation were considered: fields pointing toward and away from the duct’s center alternately, and all fields pointing toward the duct’s center. For each arrangement of magnets, various intensities of magnetic fields were considered to evaluate two effects: the influence of the magnetic field on the flow velocity, and the influence of the flow velocity on magnetic field induction. It was found that for the second arrangement of magnets and Hartmann numbers larger than 10, the flow velocity was reduced by as much as 35%, and the axial magnetic induction was as high as the field intensity applied by each magnet. Those effects were negligible for the first arrangement and low fields because of the distribution of field lines inside the duct for these situations.

In this article, we proposed a simple optical sensor for flow measurement. The sensor structure is based on an in-line Mach-Zehnder interferometer formed by splicing an input single-mode fiber section and an output single-mode fiber section to a single-mode fiber sandwiched by two multimode fibers; it is called SMSMS sensor. The proposed sensor presents a high sensitivity due to low order modes that propagate in the middle single-mode fiber (SMF) cladding. The sensor is experimentally investigated and validated in which the results show good agreement between the optical power and flow for monitoring single- and two-phase flows using water and air fluids. Using this sensor, it is possible to identify the presence of air in the pipeline, in addition to the identification of the flow pattern. Thus, the proposed sensor is an interesting alternative for measuring single- and two-phase flows.

The world is experiencing an accelerated energy transition that is driven by the climate goals to be met and that has driven the growth of different potential sectors such as electric mobility powered by electric motors, which continue to be the largest load globally. However, new needs in relation to power density, weight, and efficiency have led manufacturers to experiment with new technologies, such as rare earth elements (REEs). The permanent magnet motor is a candidate to be the substitute for the conventional induction motor considering the new editions of the IEC 60034-30-1, for which study and evaluation continue to be focused on identifying the weaknesses and benefits of its application on a large scale in industry and electric mobility. This work presents a FEM model to assess the line-start permanent magnet motor (LSPMM), aiming to simulate the behavior of the LSPMM under supply conditions with distorted voltages (harmonic content) and evaluate its thermal and magnetic performance. The model created in the FEM software is then validated by bench tests in order to constitute an alternative analysis tool that can be used for studies in previous project phases and even to implement predictive maintenance schemes in industries.

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).

In the Amazon, local electrical energy production is very important due to the large number of isolated communities, usually far from the conventional grid. In this context, steam turbines are relevant technologies to be used in the region, since waste biomass is widely available in several communities, besides, where sustainable agricultural activities take place, such as the use of açaí seeds, sugarcane bagasse, among others. Currently, there are not many relevant studies in the literature for small steam turbines operating at low pressure, conditioned to the reality of the Amazon region. Hence, the main goal of this work is to develop an experimental study of a small steam turbine operating at low pressure, in order to apply it to small energy demand, typically found in the Amazon. We developed measurements on the behavior of mechanical and electrical powers in relation to the shaft rotational speed of the small steam turbine, taking into account pressures of 0.1, 0.2 and 0.3 MPa. We concluded that the preliminary experimental survey, made in this work, demonstrates a good mechanical behavior of the small turbine and could be an alternative for electricity generation system to supply small demands in isolated communities in the Amazon, taking advantage of biomass from sustainable agricultural activities.