In opposition to the methodologies that use the frequency domain, some studies propose alternative techniques, which use low-cost sensors and perform the analysis of the signal in the time domain, as in [PERSON] and Geethanjali [13], [PERSON] and Geethanjali [14], that employed time-domain features of the vibration signals, for diagnosing bearing failure in TIMs. The characteristic selection was then carried out using a filter approach, which was implemented using a Laplacian score (LS), and a wrapper approach using a brute-force method. Thus, several pattern classifiers, such as LDA, Naive Bayes (NB), and Support Vector Machines (SVM), evaluated the selected features. Information Theory is another methodology that has stood out in fault detection and monitoring area since it can determine the redundancy between the variables involved in the problem [15]. Entropy and Mutual Information (MI) are two of the computer tools used extensively in the diagnosis of failures in electrical machines. The work of [PERSON] et al. [16] presented the Generalized Refined Composite Multiscale Sample Entropy (GRCMSE), which considers a methodology to determine the entropy of machine vibration signals to characterize the complexity of the signals at various scales and then diagnose bearing defects. In [PERSON] et al. [17], an alternative non-linear dynamic approach, called Generalized Multi-Scale Composite Permutation Entropy (GCMPE), is presented to extract bearing failure characteristics from the vibration signals of rotating electrical machines. Moreover, [PERSON] et al. [18]used the non-linear entropy characteristics, such as dispersion entropy (DisEn), permutation entropy (PerEn), and sample entropy (SampEn) of IMFs to determine the characteristics of different bearing defect severities. MI has already been used in several approaches designed to solve pattern recognition problems, such as in the work of [PERSON] et al. [4] and [PERSON] et al. [15]. Specifically, in the study of [PERSON] et al. [4], the authors devised the kernel-based mutual information matching (KEMI) function to determine the optimal parameters of Variational Mode Decomposition (VMD) from vibration signals to identify bearing defects in TIMs. The number of modes and the penalty factor greatly influence the decomposition process, so it is essential to adopt the best parameters. The experimental results, which exceeded 91% accuracy, demonstrated the promising capacity of the proposed methodology. MI can identify linear and non-linear dependencies between the time series [19]. Considering that the TIM is a complex non-linear system, in this study, unlike [PERSON] et al. [4], MI is used as a measurement of the similarity between the currents to extract meaningful information from the signals, which allows an efficient diagnosis of bearing failure in TIMs. Like signal processing tools, Intelligent Systems (IS) have proven to be important in the diagnosis of failures in electrical machines due to their effectiveness in identifying the fault without the need for complex mathematical models, simplifying the computational implementation and allowing the detection of the origin of the failures, contributing to a more reliable operation of the machines. Among the IS tools, it can be mentioned Decision Trees (DT), Random Forests, ANNs, SVMs, \(k\)-Nearest Neighbour (\(k\)-NN), among others [5, 7, 10, 13, 16, 20]. In the manuscripts of [PERSON] et al. [21] and [PERSON] and [PERSON] [22], a survey of several methodologies for the fault diagnosis in traction systems of high-speed trains were presented. It is possible to observe the wide applicability of IS tools, as well as signal processing techniques, such as FT, WT, PCA, among others, in the fault detection systems. Recently, deep learning (DL) has attracted researchers' attention in several areas, such as image processing, computer vision, and pattern recognition, due to their better understanding of the intrinsic information of the analyzed data [5].