## 1 Introduction  
One of the principal mechanical components in global industries is the rotating machine. With the rapid and daily advancements in technology, new rotating machinery tools appear that are better, more complicated, and more accurate. Therefore, their functionality affects safety and operational stability and reliable industrial production. However, due to the complexity in rotary machinery systems and the multiplicity of parts, the probability of failure is greatly increased. Among all the components in rotating machines, rolling element bearings (REBs) are used in a majority of them. Bearings facilitate the movement of components owing to the rolling action and diminished friction. Their exceptional anti-friction ability makes them a very important tool wherever there is the challenge of energy savings [1]. Among all the industrial attention, one absolute concern to ensure reliability and safety in advanced technical systems is anomaly detection. Timely and correct anomaly detection prevents disastrous events and increases production efficiency. Furthermore,immediate fault diagnosis in REBs can help to figure out the main reasons for product corruption and can avoid irregularities in the system and/or complete failure [2]. Even though fault detection and diagnosis are acutely challenging and intricate due to nonlinear parameters in the bearings and in work complexity, bearings are one of the critical components that can cause 40 to 50% of all failures in rotating machines [3,4]. Accordingly, fault diagnosis for these elements is a necessity. Within this concept, a maintenance procedure for monitoring the health situation of machinery is necessary, which makes the optimal decision to prevent or reduce faults mainly based on condition monitoring information. According to [4,5], for an efficient machinery health diagnosis program, one of the principal technical processes is data acquisition. Numerous sensors are applied over a certain time to find industrial irregularities (for example, in bearings) based on physical signals called time-series signals [6,7]. A time series mainly operates as input data with which to train models for industrial anomaly detection [2,8]. Motivated by the discourse above, the issue of condition-monitoring procedures becomes important for identifying serious changes that are expressive of a fault. Therefore, a multitude of studies have been conducted to monitor the condition of bearings [9, 10, 11, 12, 13]. Subsequently, different methods have been determined for condition monitoring in order to recognize and classify bearing faults. These methods are defined based on vibration signals, acoustic emission signals, and motor current signature analysis [14,15]. Generally speaking, faults in bearings can be classified as follows: (i) the outer bearing race fault (OURF), (ii) the inner bearing race fault (INRF), (iii) the roller fault (ROLF), and (iv) the cage fault [15,16]. To recognize, discover, and classify faults in rolling element bearings, vibration supervising is widely used and is an economical monitoring method. Nowadays, fundamental monitoring of rolling element bearings generally depends on analyzing vibration signals due to their great capabilities in describing REB performance [11]. Bearings generate vibration, either due to differing compliance or from the appearance of a crack. Numerous studies have been carried out on vibration signal analysis, and a variety of research has been conducted based on the kinds of defects and fault diagnosis techniques [8,17,18]. Based on the information provided, fault diagnosis in bearings is conducted with various methods. The manner of signal processing can be leveraged as a known procedure; other methods exist that lean on data-driven techniques and model-based techniques; finally, one further method is an amalgamation of the above techniques--the hybrid method [11,19,20]. What drives us to use the hybrid method is that these other methods have weaknesses that make them difficult to use.