## 4 Discussion
However, in industrial applications, when there is a precision value greater than 70% in relation to automatic classification problems, this is considered acceptable due to how challenging it can be to capture and represent all the possible variations that an electromechanical system can have under real-life operating conditions [15]. Another aspect to evaluate is the number of failure conditions detected since the more faults that are studied, the lower the accuracy for classification [41]. In addition, when dealing with the study of multi-faults in an electromechanical system, it is important to consider in which elements of the system the fault occurs since most only focus on the analysis of the conditions of the IM, leaving aside another important element in the form of the GB. For this work, four types of conditions in the IM (HLT, BB, OB, and MAL), as well as three conditions in the GB (healthy gears, wear in the gears by 50% and 75%), obtained a precision of 98.5%. This indicates that the proposal of new methodologies can help with the problems that arise when studying multi-faults in electromechanical systems. Accuracy is one of the most widely used indicators to measure the performance of a classification algorithm [41]. However, it is not the only indicator that needs to be calculated. This research proposes other indicators to be measured, such as precision, recall, and the F1-Score, which allow the performance of the CNN-based classification algorithm proposed for this work to be known. Based on the results, the best performance of the indicators was precision = 98.53%, recall = 98.65%, and F1-Score = 98.55%. In this way, a more complete overview of the performance of the proposed methodology is shown. In addition, when wanting to make a comparison with other methodologies related to Table 1, it is not possible because they do not report indicators other than accuracy. Finally, another important point to discuss is that the methodologies presented require a series of steps that make their implementation laborious and complex since they use image processing methods to segment hotspots, apply techniques for feature extraction and reduction, and algorithms that allow the classification of the state of the electromechanical system [17; 18; 19; 20]. Having problems, such as the computational time required for the training and execution of the proposed methodologies, is significant with respect to the methodologies that implement fewer steps. On the contrary, the proposal of this work considers the architecture of the CNN for the direct processing of the thermographic image, thus having a lower computational load than that required by methodologies that use multiple steps. Furthermore, it is important to mention that of the proposals mentioned in Table 4, none addressed the inevitable corruption of noise, as proposed in this work. Having the design of a robust and reliable methodology that can be implemented in real-life scenarios is important. On the other hand, another advantage is that the proposed method can be used as a basis for other applications where thermographic images are used, such as the detection of electrical faults in induction motors. However, it is necessary to make the appropriate configurations and train the system. For this purpose, the best hyperparameters must be obtained through ablation tests. Lastly, this paper proposes a novel methodology that combines the analysis of thermographic images and convolutional neural networks (CNNs) for the automatic diagnosis and classification of multiple faults in the induction motor, as well as different levels of uniform wear conditions in a gear transmission system. Like vibration and acoustic technologies, thermography is a non-invasive technique that provides a discreet and convenient solution for diagnostics. This technology allows data to be collected without the need for direct physical contact or engine shutdown.