## 1 Introduction
In this regard, in the research field, the faults produced by electrical erosion are artificially produced by drilling a hole in the outer race, inner race and/or by damaging the rolling elements of the bearing [29; 30; 31]. Additionally, most of the times, the initial damage of bearings can start with small holes that may grow gradually until producing irreversible damages; thereby, the importance of studying the evolution of faults in bearings is an interesting topic that should continue to be studied. On the other side, it should be highlighted that most of the reported works in regard to the detection of different severities of bearing faults are validated under experimental datasets, where critical damages are produced on the bearing elements, i.e., excessive damages produced by drilling holes in the outer race with a diameter higher than 6 mm [32]. During the analysis of the occurrence of bearing faults, it would be interesting to study the evolution of the fault occurrence, which is a more gradual effect that can happen in industrial environments. The proposal of condition monitoring strategies have been supported by the use of different signal processing techniques that can be based on different domain analyses, i.e., time-domain [33; 34], frequency-domain [21], and time-frequency domain analysis [35]. For the time-domain analysis, the estimation of statistical time-domain features is taken into account for many of the proposed diagnosis methods. Statistical time-domain features are preferred due to its simplicity of calculation and its effectiveness to model changes and trends of signals [18; 33]. Despite that, most of the classical approaches aim to perform the diagnosis of bearing faults by carrying out the analysis of the characteristic fault-related frequency components through the FFT (Fast Fourier Transform) [34]. Furthermore, although the analysis of the fault-related frequency components produced by bearing failures leads to achieving the occurrence of faults, additional knowledge and experience is necessary to distinguish between multiple frequency components that tend to appear as the fault-related frequency patterns. Additionally, time-frequency analysis represented by complex techniques, such as the Hilbert transform, has been included in condition monitoring strategies [8]. In this regard, Artificial Intelligence (AI) algorithms and Machine Learning (ML) techniques have been also incorporated in condition-monitoring strategies;thus, diagnosis and classification structures can be based on Neural Networks (NNs), Support Vector Machines (SVMs), decision trees, among others, to achieve the automatic fault diagnosis [16; 35; 36]. Conversely, the use of ML techniques is implemented as feature extraction and feature reduction techniques [37]. Even heuristic search algorithms, such as Genetic Algorithms (GA), are also used in condition monitoring approaches to solve optimization problems that commonly focus on procedures to select those significant features [38]. Accordingly, the consideration of both AI algorithms and ML techniques in condition monitoring strategies can lead to improve the resulting fault diagnosis; even more, if the proposal of condition monitoring methods is based on the fusion of information. Bearings are the elements that allow the rotating movement in rotating machines, and the sudden occurrence of faults is inherent to its operation. Additionally, the occurrence of faults in bearings may initially occur by its degradation, and then, several damages may appear over the inner race, outer race, and the balls of the bearing. Thus, there are different fault severities that occur from the initial failure to its most critical degree. In this regard, a condition monitoring methodology designed for identifying different severities of the outer race bearing fault in an IM is proposed in this work. The proposed method is supported by tools, such as Machine Learning (ML) techniques and Artificial Intelligence (AI), that facilitate the fusion information of different physical magnitudes.