## 1 Introduction  
Among the various failures of induction motors, the most common defect is bearings. Bearing failures shows a high percentage of failures when compared to other faults, such as short-circuits, broken rotor bars, and eccentricities [1]. The role of the bearing is not limited to electrical motors but also extends to various mechanical components, and it stands to be a vital component. The early identification of bearing faults may prevent and reduce high mending costs and maintenance costs. Additionally, the early prevention of faults may have the effects of not decreasing the production quantity and not extending breaktime. Thus, induction motor conditions, especially bearing conditions, should be monitored for healthy production. In most existing studies, pitting has been considered a fault factor, and vibration-based analyses have been used primarily to diagnose bearing faults [2]. However, the acquisition of vibration data requires transducers or accelerometers, which makes this method expensive. An alternative method, namely, motor current signature analysis, is easier to implement and more affordable because it eliminates the positioning of sensors [3]. However, this method cannot identify the present state of a motor. Over the past years, acoustic emission (AE) sensors have been used in bearing condition monitoring and fault diagnosis [4]. AE signals can capture bearing fault features and detect incipient faults [5, 6]. Detection is performed using the frequencies of selected components as key features. For rotating machinery systems, the Hilbert Huang transform (HHT) is used with AE signals to detect faults [7]. The application of digital signal processing has been extended to identify the bearing failure of induction machines [8]. Generally, raw signals are insufficient to identify failures. Therefore, for fault identification, features are extracted from the time and frequency domain analyses. The root means square method [9], high-order statistical method [10], and short impulse method [11] are examples of time domain analysis approaches. A linear discriminant analysis was also performed on stator currents for fault diagnosis, and further diagnosis was conducted with a Bayesian classifier [12]. This technique recognized damage with large-diameter pitting. Infrared thermography fault diagnosis methods based on image processing have also been proposed [13]. However, their usage has been limited, and successful results were observed only in DC machines [14]. In wound-rotor induction motors, infrared data analysis provided useful information about defects and present motor conditions [15]. In most cases, this method is applicable and is performed offline. Although infrared techniques have immense potential for fault detection, their cost is high due to the need for data acquisition devices and infrared cameras [16]. A comparison study on vibration and current signal analysis was published [17]. The number of intelligent diagnosis methods for identifying defects is rapidly increasing, and adaptive neuro-fuzzy inference systems [18; 19; 20; 21; 22; 23] and artificial neural networks (ANNs) [19; 20; 21; 22] have often been applied. In general, intelligent bearing fault diagnosis involves two steps: feature extraction and classification [19]. However, intelligent methods used in bearing fault diagnosis have some inherent disadvantages. Most of the fault categories belong to a single pitting (hole) fault, and compound faults have been discussed in some cases. Moreover, most intelligent systems are nonlinear and belong to shallow learning models. Their accuracy varies with the selection of features, largely depends on the diagnosis model, and requires engineering experience [24]. Some authors published a pioneering work on failure diagnosis using deep learning [25]. Recently, support vector machines (SVMs) have been widely used to diagnose stator winding faults [26; 27] and wind turbine issues [28].