## 1 Introduction  
Among rotating machinery, induction motors (IM) are broadly used in manufacturing industries such as transportation, petrochemicals, and power systems due to low cost, high reliability, robust design, and higher efficiency under full load. Generally, IMs need to remain operative for a long duration and commonly under harsh operating environments accompanied with regular wear, which cause mechanical and electrical stresses. These can lead to unexpected failure in gears and bearings, which are the significant machine components of IM. Such failures could cause financial losses or human causalities. Therefore, machine health monitoring and fault analysis become an integral part of the maintenance system in an industrial environment. A robust condition monitoring system can decrease maintenance charges, improve productivity, and increase reliability and safety. In recent times, the machine learning (ML)-based fault analysis approaches are turned out as a powerful and prevalent approaches in the area of continuous health monitoring of rotating machineries, as they have the ability to extract valuable information from the considerable amount of historical data [1; 2; 3]. Rotor faults, stator faults, rolling element bearing (REB) faults, and other faults commonly occur in IMs, among them bearing faults are most frequent. Statistics shows that 40% of total breakdown situation in large size machines occurred due to bearing fault and the number is as high as 90% in case of small machines [4]. The main elements of a bearing are the inner, and outer ring, rolling element,and cage. Faults in bearings may occur during manufacturing or after some period of operation. If a defect occurs on a contact surface of the bearing elements, an impact is generated when the bearing hits the defect, which results a strident transient response followed by damped oscillation. Generally, the periodic transient behavior contains important information regarding bearing health [5]. Therefore, detecting the transient response and accurate analysis of the signal can successfully forecast bearing faults in the early state. This is crucial for preventing unpredicted downtime of rotating machines and for confirming safety without affecting the efficiency of the industrial process. Hence, real-time monitoring and diagnosis approaches for REB have gained considerable attention from researchers in recent years. Reactive, preventive, and predictive maintenance are the three primary approaches for fault diagnosis. In reactive maintenance, the fault occurs, and then initiates corrective measures, which results in failure of the machine, high cost of components, reduced safety, and unanticipated downtime. The preventive maintenance process aims at a time bound procedure and designs a corrective action periodically without knowing the real time condition of the REB, which requires many resources and a lower efficiency. Lastly, predictive maintenance is designed based on real-time monitoring and performance of necessary actions when the REB shows a certain behavior that indicates failure or diminished performance [6]. From another point of view, fault diagnosis can be divided into model-based and knowledge-based methods. The system faults are analyzed by an equivalent mathematical model of the system; however, this can be complicated by system non-linearity. On the other hand, knowledge-based methods are developed depending on measurements, experience, data analysis, and physical assessment of the target structure [7]. Developments in sensors, communication technology, availability of data, and an efficient data processing algorithm have suggested data- or knowledge-based fault diagnosis condition monitoring system as a more acceptable choice. The data-driven fault diagnosis system, data acquisition, data preparation, construction of feature matrix, and fault classification are the four most popular phases.