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In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

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The use of UAV (unmanned aerial vehicle) technology has allowed for advances in the area of robotics in control processes and application development. Such is the case of image processing, in which, by the use of aerial photographs taken by these aircrafts, it is possible to perform surveillance and monitoring tasks. As an example, we can mention the use of aerial photographs for the generation of panoramic images through the process of stitching images without losing image resolution. Some applications are photogrammetry and mapping, where the main problems to be solved are image alignment and ghosting images, for which different stitching techniques can be applied. These methodologies can be categorized into direct methods or feature-based methods. This paper aims to show an overview of the most frequently applied mosaicing techniques in UAVs by providing an introduction to those interested in developing in this area. For this purpose, a summary of the most applied techniques and their applications is given, showing the trend of the research field and the contribution of different countries over time.

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