Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.

Knee injury is a common health problem that affects both people who practice sports and those who do not do it. The high prevalence of knee injuries produces a considerable impact on the health-related life quality of patients. For this reason, it is essential to develop procedures for an early diagnosis, allowing patients to receive timely treatment for preventing and correcting knee injuries. In this regard, this paper presents, as main contribution, a methodology based on infrared thermography (IT) and convolutional neural networks (CNNs) to automatically differentiate between a healthy knee and an injured knee, being an alternative tool to help medical specialists. In general, the methodology consists of three steps: (1) database generation, (2) image processing, and (3) design and validation of a CNN for automatically identifying a patient with an injured knee. In the image-processing stage, grayscale images, equalized images, and thermal images are obtained as inputs for the CNN, where 98.72% of accuracy is obtained by the proposed method. To test its robustness, different infrared images with changes in rotation angle and different brightness levels (i.e., possible conditions at the time of imaging) are used, obtaining 97.44% accuracy. These results demonstrate the effectiveness and robustness of the proposal for differentiating between a patient with a healthy knee and an injured knee, having the advantages of using a fast, low-cost, innocuous, and non-invasive technology.

Nowadays, induction motors and gearboxes play an important role in the industry due to the fact that they are indispensable tools that allow a large number of machines to operate. In this research, a diagnosis method is proposed for the detection of different faults in an electromechanical system through infrared thermography and a convolutional neural network (CNN). During the experiment, we tested different conditions in the motor and the gearbox. The induction motor was operated in four conditions, in a healthy state, with one broken bar, a damaged bearing, and misalignment, while the gearbox was operated in three conditions with healthy gears, 50% wear, and 75% wear. The motor failures and gear wear were induced by different machining operations. Data augmentation was then performed using basic transformations such as mirror image and brightness variation. Ablation tests were also carried out, and a convolutional neural network with a basic architecture was proposed; the performance indicators show a precision of 98.53%, accuracy of 98.54%, recall of 98.65%, and F1-Score of 98.55%. The system obtained confirms that through the use of infrared thermography and deep learning, it is possible to identify faults at different points of an electromechanical system.

La natación es un deporte que demanda la activación de distintos grupos musculares. Una demanda excesiva puede repercutir directamente en la condición del nadador. Por ello, es importante monitorear la actividad muscular. Este estudio tuvo por objetivo evidenciar las diferencias y similitudes de la activación muscular a través de la termografía en los distintos estilos de natación. Se identificó la activación de los diferentes grupos musculares que participan en cada gesto técnico, de cada uno de los estilos de nado. Asimismo, se analizó el aumento térmico en el requerimiento energético de dichas zonas musculares. Se estudiaron diez nadadores de alto rendimiento; cinco hombres y cinco mujeres, quienes fueron divididos de acuerdo con la prueba que realizaron, la velocidad o el fondo. Se usó una cámara termográfica para la recolección de las muestras. Entre las ventajas de esta tecnología se tiene que es una herramienta no invasiva e innocua. Las imágenes se tomaron en un tiempo efectivo de intervención. Se compararon dos grupos musculares: el bíceps del tren superior y el cuádriceps del tren inferior. Durante las dos sesiones de estudio, la alberca estuvo a una temperatura promedio de 28°C±1°C. El análisis de temperaturas antes de las pruebas muestra una temperatura máxima alcanzada de 32°C±1°C por cada grupo muscular estudiado; la diferencia de temperatura entre el bíceps y el cuádriceps fue de 1 °C ±1°C. El conocimiento de la demanda muscular por zonas puede hacer más objetivo el entrenamiento.