## 1 Introduction
On the other side, the identification of faults in bearings can be also performed through classical approaches based on MCSA (Motor-Current Signature Analysis); in this sense, in [24], stator current-based bearing fault diagnosis using fractional wavelet denoising and deep learning algorithms is presented; although through the proposed method is possible to identify the damage of outer and inner races of bearings, the application of the methods is focused on the detection of a unique fault severity. Likewise, in [25], a novel methodology for fault size estimation of ball bearings using stator current signals is proposed, where the fault-excited harmonic components and amplitudes in stator current are estimated, and the FHD (Fault-excited Harmonic Distortion) evaluated to achieve the fault size estimation. Even though different fault sizes can be detected, the estimation of undesired frequency components that may appear overlapped with the fault-related components may lead to infeasible detections. Therefore, the separate analysis of vibrations and stator currents may lead to the identification and detection of bearing damages, and the analysis of both signals present particular advantages that may contribute to improving the condition assessment of bearing faults if both signals are analyzed together. Although several works have been proposed to perform the bearing fault detection and diagnosis, some challenges still exist that have not been addressed in the field of bearing fault assessment, which are (i) the bearing fault detection from the viewpoint of severity detection has not been extensively studied; thus, the identification of fault graduality may be difficult whether physical magnitudes, such as stator current, show small changes and/or variations in amplitude,and (ii) the occurrence of bearing faults may be confused with other faulty components since fault-related patterns overlapping is a common effect in rotating machines. Additionally, it must be mentioned that others have tried to overcome these challenges through ensemble learning, parallel Neural Networks, and transfer learning-based solutions; however, complex condition monitoring structures are needed to achieve good performance results. In this regard, for example, in [26], a diagnosis method that first includes a higher-order spectral analysis and multitask learning-based convolutional neural network is proposed for the identification of the bearing health condition, and second, identifying bearing fault conditions by means of a transfer learning-based approach in the presence of multiple crack conditions. However, although this proposal leads to the efficient diagnosis of bearing faults, prior knowledge about the implementation and configuration of deep learning (DL) techniques is the main limitation to develop complex structures that are also based on ensemble learning approaches [27]. Moreover, recent DL-based approaches have to overcome some challenges that must be avoided for practical applications. These limitations are: (i) the operation is limited to the amount of data that are not easy to obtain in industrial sites; (ii) difficulties and unclear understandable interpretation for practitioners; (iii) generalization-based approaches to avoid overfitted responses and solutions; and (iv) the strategical hyper-parameter tuning procedures to enable its easy access and configuration. Nevertheless, even though good performance results are obtained, the use of ensemble learning-based on DL results in a complicated task; thus, these challenges can be overcome by the proposal of strategies that incorporate proper processing techniques, i.e., analysis in the time-domain and frequency-domain, and by considering multiple signals that contain different information, but its fusion leads to highlighting those representative fault-related patterns. Most of the proposed studies that have analyzed and identified the occurrence of faults in bearings are commonly focused on analyzing those faults that are related to the electrical erosion phenomenon [13; 15; 28].