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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.

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This work presents a device capable of performing sanitization in environments using an electrostatic filter and ultraviolet light. Recognized techniques were employed in an innovative and low-cost construction model, like Corona Discharge and UVC germicidal light, using elements of easy acquisition and construction. The device consists of a tube with several electrostatic filters. On one side of the tube, an exhaust fan is installed to collect the air to be disinfected. So the air passes through the tubes with the electrostatic filter, which is responsible for collecting the particles suspended in the passing air, attracting them to the metal surface. Allied with this, UVC lights are also inserted in the structure, ensuring greater effectiveness in decontaminating the air that passes through the tubes. In the posterior side to the exhaust fan, the disinfected air is returned to the original environment. A Fluid dynamics simulation was made to investigate the airflow in a room. Standard strains for antimicrobial susceptibility testing were used for bioaerosols assay. The result is a low-cost equipment with great potential to deactivate several kinds of viruses, most especially the Sars-CoV-2 (Coronavirus), that is the main responsible for the global health crisis, designed to be used indoors with low ventilation.