Temperature measurement by infrared thermography is a powerful technique for predictive maintenance. To make accurate diagnoses, errors associated with temperature measurement results should be minimized because the diagnosis is obtained by applying temperature difference criteria in consecutive measurements or in similar equipment. Temperature measurement by infrared thermography is susceptible to errors caused by many things. We study and assess the effectiveness obtained in use of experimental models for error correction in temperature measurement by thermal imagers due to the influence of the field of view of the imager's lens, combined with varying viewing angles between the analyzed object and the imager. The results indicate that these factors can introduce errors that compromise the diagnosis. Experimental models based on artificial neural network and on regression were developed to correct such errors.

Temperature measurement by infrared thermography is a technique that is widely used in predictive maintenance to detect faults. The uncertainty involved in measuring temperature by thermography is not only due to the imager, but also due to the measurements and estimates made by the user: emissivity of the inspected object, distance, temperature, and relative humidity of the propagation medium, temperature of objects located in the ambient, and the imager itself. This measurement uncertainty should be available for the thermographer to be able to make a more accurate diagnosis. The methods available in the literature to estimate the uncertainty of measured temperature usually require information nonaccessible to the regular thermographer. This paper proposes a method for calculating the uncertainty of temperature that requires only data available to the thermographer. This method is useful under usual conditions in predictive maintenance—short distance (7.5 to 14 μm) thermal imagers, no fog or rain, among others. It provides results similar to methods that use models that are not available or reserved by the manufacturers of imagers. The results indicate that not all sources of uncertainty are relevant in measurement uncertainty. However, the total uncertainty can be so high that it may lead to misdiagnosis.

The COVID-19 pandemic has detrimentally affected people’s lives and the economies of many countries, causing disruption in the health, education, transport, and other sectors. Several countries have implemented sanitary barriers at airports, bus and train stations, company gates, and other shared spaces to detect patients with viral symptoms in an effort to contain the spread of the disease. As fever is one of the most recurrent disease symptoms, the demand for devices that measure skin (body surface) temperature has increased. The thermal imaging camera, also known as a thermal imager, is one such device used to measure temperature. It employs a technology known as infrared thermography and is a noninvasive, fast, and objective tool. This study employed machine learning transfer using You Only Look Once (YOLO) to detect the hottest temperatures in the regions of interest (ROIs) of the human face in thermographic images, allowing the identification of a febrile state in humans. The algorithms detect areas of interest in the thermographic images, such as the eyes, forehead, and ears, before analyzing the temperatures in these regions. The developed software achieved excellent performance in detecting the established areas of interest, adequately indicating the maximum temperature within each region of interest, and correctly choosing the maximum temperature among them.

Performance of photovoltaic generators is affected by, among other factors, surface dust deposition on photovoltaic modules, which causes uniform soft shading, reducing the solar radiation reaching photovoltaic cells. Several researches have demonstrated this effect through studies in specific localities. As deposition of particulate matter varies according to locality, these results cannot be generalized or used in other places, since these studies do not characterize their local dust. Considering these research opportunities, this paper evaluates the deposition effects on photovoltaic modules by different types of dust from anthropogenic activities recurrent in urban areas, a typical situation of distributed microgeneration. Particulate matter of iron ore, building construction, quarry, and mineral coal are characterized morphologically and dimensionally. Then, their effects on photovoltaic module performance are analyzed under different deposition densities according to typical urban deposition rates. The results show that the different dust materials studied attenuate the power generation capacity to different extents, and superficial dust density of the particulate matter also produces different effects. These results can be used in performance and economic feasibility analyzes of photovoltaic generators in different locations with similar pollution characteristics.