Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

In this work, we present the design, laboratory tests, and the field trial results of a power-over-fiber (PoF) low power instrument transformer (LPIT) for voltage and current measurements in the medium voltage distribution networks. The new proposed design of this power-over-fiber LPIT aims to overcome the drawbacks presented by the previous technologies, such as the continuous operation (measuring and data transmission) for a wide current range conducted in the medium voltage transmission lines, damage due to lightning strikes, accuracy dependency on vibration, position and temperatures. The LPIT attends the accuracy criteria of IEC 61869-10 and IEC 61869-11 in terms of current and voltage accuracy and it attends the practical criteria adopted by Utilities companies including voltage measurements without removing the coating of the covered conductors. The PoF based LPIT was developed to be applied at 11.9 kV, 13.8 kV, and 23.0 kV phase-to-phase nominal voltages, and in two current ranges 1.25–30 A and 37.5–900 A. The digital data transmission of current, voltage, and temperature from the sensing unit to the processing unit uses a special synchronism technique and it is performed by two 62.5 µm multimode fibers in 850 nm. The optical powering in 976 nm is also performed by one 62.5 µm multimode fiber from the processing unit to the sensor unit. We presented all details of the sensor design and its laboratory characterization in terms of accuracy and temperature correction. We also presented the results of field tests of the sensor made in two different conditions: in a standard distribution network and an experimental hybrid fiber/power distribution network. We believe that these studies aim to incorporate optical fiber and devices, digital technologies, communications systems in electrical systems driving their evolution.

Open-source hardware and software platforms have played an important role in democratizing access to technology and education in computer science and engineering. Recent advancements in tools, such as KiCad, MicroPython, and Thonny, integrated development environment potentially accelerate low-budget educational applications, providing a smooth and consistent learning curve for users. The BitDogLab, utilizing these platforms, is designed and developed for Science, Technology, Engineering, Arts, and Mathematics activities based on the Raspberry Pi Pico Board, featuring artificial intelligence (AI)-supported programming via the GPT model. BitDogLab is an open-source hardware solution with a flexible design for continuous expansion in both software and hardware areas. Its functionality, based on open-source code, can be enhanced by integrating various software resources. Additionally, BitDogLab’s adaptability to sophisticated hardware like robotic systems or advanced sensors expands its educational applications. An evaluation of BitDogLab, conducted through a hands-on workshop at the Latin America Open Technology Conference—Latinoware 2023, revealed unanimous interest among 36 respondents, mainly for teaching programming (58%), and valuing its low cost, diverse components, and ease of AI-assisted programming. 75% of respondents expressed a strong desire to use BitDogLab, despite 19% preferring familiar boards like Arduino or Micro:bit. The project’s open distribution encourages community contributions, promoting collaborative learning and innovation from children to preuniversity students.

We propose and experimentally demonstrate a spatially tunable phase-OTDR based distributed microphone for listening to the sound of multiple machines. The distributed acoustic sensing capability, allied with the real-time spatial tuning, enables listening to a drill and to a cooling water system pump placed in two different sections along a single optical fiber, one at a time. The recorded acoustic waveform profile of both machines agreed with their operating cycles. Moreover, the sounds generated by both engines are successfully distinguished with the proposed method even when both machines are operating simultaneously.

This work presents a failure diagnosis tool for a water pump using a low-cost MEMS accelerometer. It was inserted three types of failures: rotor blade (new and damaged), pump soleplate tightness (stiff or loose), and cavitation, in this case on three conditions: none, incipient and severe, totaling twelve fault combinations. These conditions were tested under two different speeds to perform the diagnosis, totaling twenty-four tests. In all cases, the vibration signals from axes X, Y, and Z were acquired. Some features extracted from the vibration spectra from X-axis were used to compose the dataset. These data were analyzed employing logistic regression, a linear support vector machine (SVM), and an artificial neural network multilayer perceptron (ANN-MLP). We compared these three techniques of machine learning and evaluated which one was able to obtain the most accurate result. Using the ANN-MLP, the system was able to detect all three types of failures inserted, with about 100% of accuracy on the rotor blade condition, 92% for anchorage faults, and about 99% accuracy on cavitation state. As a conclusion, it is demonstrated that this classifier algorithm can be used to process the data from the low-cost MEMS accelerometer in predictive maintenance as an accurate tool.