## 1 Introduction  
Induction motors are essential elements in industries and are used in a wide range of applications. Despite being robust, these machines are prone to faults in different parts and components, with the bearings being responsible for up to 45% percent of all the faults in electric motors [1]. Bearings are one of the most important parts of electric motors, as they allow the movement of the rotor and the transmission of the torque to the kinematic chain. They are subjected to continuous loads, stresses, impacts, environmental conditions, and inadequate manipulation. All these factors play a part in the various mechanisms that cause bearing failure, making it imperative to identify early signs of deterioration to prevent downtime and extended damage to the machines. For this reason, several researchers have studied the mechanisms causing the bearings to fail, classifying the causes as fatigue, wear, corrosion, deformation, fracture, and incorrect handling and maintenance [2; 3; 4; 5; 6; 7; 8]; improper handling and maintenance is one of the most important and preventable mechanisms, as it impacts the lubrication of the bearing and up to 52% of their faults can be due to lubricant contamination [4]. At the same time, one of the biggest challenges for the detection of these faults is the correct diagnosis, as it involves the accurate measurement of physical variables that provide significant information regarding the faults and the development of methodologies considering the correct manipulation and analysis of the data to discern between conditions involving the presence of different severities of a particular defect as well as the combination of faults in one bearing. Over the years, several methodologies have been developed to mainly diagnose defects in the inner and outer races of bearings, measuring a variety of physical magnitudes that are analyzed by various processing tools and algorithms to determine the presence of the faults. In this regard, temperature is a novel approach to bearing failure diagnosis that shows promising results; the authors of [9] developed a methodology to diagnose between five different one-severity damaged bearings and a healthy one using infrared thermography and a Convolutional Neural Network (CNN), achieving 99.8% effectiveness in the diagnosis with the noninvasive method; however, they noted the drawbacks of the sensitivity to the installation of the equipment and electromagnetic interference. On the other hand, current signal analysis is a methodology that has proven efficiency in bearing diagnostics; in reference [10], the authors focused on characteristic fault frequency components in a stator current to detect different severities of outer race (OR) damage using the Fast Fourier Transform (FFT), being able to detect the defects through the analysis of the magnitudes across all of the current signal spectrum to relate the increase in magnitude of specific components to the severity of the fault. On the other hand, the authors of [11] employed the Discrete Wavelet Transform (DWT) to diagnose one graduality of inner race (IR) and OR defects by separating the measured current signals into frequency bands and calculating the energy in the bands to relate the results to the damage. Similarly, stray flux analysis has grown in popularity in recent years in the condition monitoring field; the authors of [12] focused on the detection of two levels of damage in the OR of a bearing by the analysis of one stray flux signal using thresholds on the kurtosis values to distinguish between conditions, developing a system for online use on a Field Programmable Gate Array (FPGA) platform. It must also be noted that vibration signal analysis is the preferred method to study bearing faults; researchers in [13] utilized a CNN to analyze vibration data to detect OR and IR defects with different loads and speeds, achieving 93.97% in their classification efficiency.