## 1 Introduction
Currently, IRT-based condition monitoring represents a novel and technological solution that can be implemented to monitor the thermal conditions of electromechanical systems to identify faults that could be generated, mainly in elements such as IM and GB [6; 13]. In the literature, methodologies are implemented based on thermographic images to manually monitor the conditions of electromechanical systems; such methods imply simple or directly interpreting the thermographic image, or with the help of commercial software, an analysis is made. And based on the operator's experience, a diagnosis is issued that defines the condition of the equipment [14; 15]. Although this technique is one of the most applied in the industry, most of the time, it results in considerable time being invested to reach a result. In addition, the interpretation of results can be wrong because not everyone can perform them. This analysis requires training and experience, and the operator must ensure a correct diagnosis and not make mistakes with the information. Another method used where thermographic images are involved for monitoring conditions in electromechanical systems is based on semi-automatic techniques, where algorithms mainly based on image processing methods that help to detect are implemented to perform the analysis and interpretation of regions of interest from the image and then automatically extract information [13; 16]. However, the interpretation of information is carried out by the operator, who must have the experience and training to be able to generate a correct diagnosis of the condition of the equipment. Once this type of methodology has been implemented, knowledge of image processing is generally required to generate the algorithms, as well as being trained and having the experience to be able to interpret the results that lead to a correct diagnosis. Finally, with the scientific/technological advance, methodologies based on infrared thermography have been proposed that allow the monitoring of conditions in faults in automatic electromechanical systems, where through computational algorithms based on image processing methods, advanced statistics, machine learning, and artificial intelligence, among others, information can be detected, extracted, and classified automatically [17; 18; 19; 20]. This facilitates the interpretation of information for the diagnosis of the equipment, as they are automatic techniques that do not require trained or experienced personnel. Furthermore, it can result in faster methodologies than previous methods, depending on the computational load at which they work. In this sense, different methodologies based on artificial intelligence and infrared thermography have been reported in the literature that allow for the automatic detection, extraction, and classification of faults in electromechanical systems. For the detection of regions of interest in thermographic images, most of the methodologies report techniques based on image processing and intelligent algorithms that allow the automatic segmentation of hotspots without the intervention of human experience, methods based on thresholding, edge detection, and RGB color, among others [5; 13; 16; 21]. These works are mostly used for automatic segmentation. Once the regions of interest have been detected, features of the hotspots are extracted, which allows the analysis of the conditions of the equipment. For this, methods for the reduction and selection of representative features are implemented, and among the most used are Scale Invariant Feature Transform (SIFT), Speed-Up Robust Features (SURFs), Principal Component Analysis (PCA), Local Octa Pattern (LOP), and Linear Discriminant Analysis (LDA), among others [17; 22; 23; 24]. Finally, with representative features, methods based on artificial intelligence are implemented with supervised and unsupervised learning for the automatic classification of failures in electromechanical systems.