## 1 Introduction  
A great deal of the applications in the modern industry is composed of electromechanical systems or kinematic chains that generally involve electrical rotating machinery. In fact, it has been reported by the International Energy Agency (IEA) that electric motors demand more than half of the global electricity [1, 2]; additionally, it has been estimated that such electrical demand may grow approximately 2.1% per year. On the other side, it is expected that electric motors used in the industry will represent more than 30% of the total growth until 2040 [2, 3]. In this regard, the Induction Motor (IM) is the most used electric machine in the industrial sector; although IM is included in a wide range of industrial applications due to its reliability, robustness, and efficiency, the occurrence of faults during its operation represents a critical issue since unexpected stoppages are produced and affect the efficiency of the production lines [4, 5, 6]. Consequently, aiming to face these issues, the development of condition monitoring strategies focused on fault detection and identification in IM, and its linked components, is being one of the priorities addressed by new research works. The IM faults occurrence is commonly produced by electrical or mechanical stresses that finally produce damages over the mechanical or electrical parts of the IM. Thus, within the most common faults that influence the IM operation are those damages that affect the stator, rotor, and the bearing elements [7; 8; 9]; specifically, the occurrence of faults in IMs is mainly associated with these elements (stator, rotor, and bearings) representing around 32%, 10%, and 40%, respectively [10; 11]. That is, a high percentage of fault occurrence in IMs is produced by its associated bearings; consequently, bearings play an important key role that may affect the operation, reliability, and efficiency of IMs. As it has been reported in the literature, bearings have finite life that is limited by their resistance to fatigue; thus, the occurrence of faults is inherent to its operation, even under ideal operating conditions [12; 13]. In this sense, most of the common faults in bearing may be attributed to the following causes: (i) overload, which may be generated by static load and/or unbalances or misalignments; (ii) excessive or insufficient lubrication; (iii) external contamination; (iv) inappropriate installation and/or design; and (v) electrical discharges passing through the bearing [14; 15]. Hence, electrical discharges are considered one of the most critical causes that may produce the gradual degradation of bearings in IMs. A large number of diagnosis strategies have been proposed and focused on the identification and diagnosis of bearing failures, where the acquisition of different signals such as the occurrence of vibrations, stator current signatures, voltages, temperatures, and thermal imagining have been analyzed aiming to provide the condition assessment of bearings [16; 17; 18; 19]. Despite different signals may be analyzed to achieve the fault diagnosis in bearings, the vibrations signals remain the most preferred that has been accepted by the industrial sector [5; 20; 21]. In this regard, in [22], a diagnosis strategy to identify impulsive bearing failures from variable-speed vibrations by means of measuring the short-term spectral components from vibration signals is proposed; although effective results are obtained through the application of the method, additional knowledge is mandatory for setting the optimum parameters that are required for the considered techniques in order to obtain high-performance results. Similarly, in [23], the combination of the spectral and permutation entropy of vibrations signal for assessing different types of damage on bearings during low-speed conditions is analyzed; despite different bearing conditions may be assessed, the proposed method seems to be designed to face the fault identification for specific operation conditions, such as continuous speed and load.