## 1 Introduction
Among the most implemented are Decision Trees, k-Nearest Neighbor (kNN), Linear and Logistic Regression, Naive Bayes, Support Vector Machine (SVM), k-means, and Multilayer Perceptron (MLP), among others [19; 20; 21; 22; 23; 24]. More recently, innovative techniques based on deep learning have been effectively incorporated into condition monitoring strategies for accurate fault detection and classification. In their studies, [PERSON] et al. [25] and [PERSON] et al. [26] employed IRT in conjunction with a convolutional neural network (CNN) to ascertain the condition of bearings. They achieved accurate rates ranging from 90 to 98%, contingent on the extent and location of the bearing damage. In a recent study, [PERSON] et al. [27] employed a novel technique to diagnose faults in rotating machinery. This involved the use of a convolutional neural network (CNN) to extract crucial fault features from captured infrared thermal images. [PERSON] [28] introduced a new method of feature extraction, PNID (power of normalized image difference), which was used in conjunction with deep neural networks to analyze thermal images of a faulty shaft in a BLDC (brushless direct current electric) motor. The analysis identified ball faults, outer race faults, inner race faults, cage faults, and healthy conditions. In this sense, from the presented works, the following conclusions can be drawn: (1) To the best of the author's knowledge, there is a lack of studies analyzing the presence of multiple faults in electromechanical systems. (2) When deep learning approaches are employed, data augmentation is not considered. Furthermore, most methodologies only use deep learning to extract features from the thermographic image and implement another algorithm for calcification, which makes the methodology more difficult to understand. (3) The potential for noise corruption in thermal images is not addressed. Based on the above, the convolutional neural network (CNN) has become one of the most commonly used techniques for fault classification from infrared thermography. This technique allows the direct evaluation of thermographic images, obtaining precise results for extraction, selection, and classification [29; 30; 31; 32; 33]. Although several methodologies have been reported in the literature that involve previously mentioned techniques for the detection of faults in electromechanical systems and that have obtained acceptable results, they are still lacking in some respects and can be improved. Most of the methodologies proposed in the literature only focus on analyzing a single element of the electromechanical system with infrared thermography. For instance, when IMs are analyzed, condition monitoring is focused on only one element of this equipment, and among the case studies available, they only focus either on the stator winding, the bearings, or the broken rotor bar, among others and identify if they have any faults [17; 18; 19; 20; 21; 22; 23; 24]. Another element of equipment in the electromechanical system that is also monitored is the GB; here, in the same way as the previous one, studies only focus on a single fault condition, such as gear wear, broken gear teeth, cracks in the gear, among others [6]. Thus, very complex methodologies that only allow the detection of a single failure condition waste the capacity that the techniques implemented in these methodologies may have [17; 18; 19; 20; 21]. Likewise, a complex and non-optimized methodology can lead to a considerable computational load. In addition, most of the methodologies proposed in the literature focus on studying multi-faults and infrared thermography that can occur in the induction motor without analyzing other elements of the system. Another important point is that there are few methodologies that carry out a study of combined failures that could occur in electromechanical systems, such as IM and GB since it is well-known that when a failure occurs and it is successful, it can propagate through the electromechanical system.