safety, property, and even the loss of lives in some cases. Hence, bearing fault detection and diagnosis have attracted researchers and scientists and have become essential for scientific advancement [3; 4]. With the growing concept of Industry 4.0 and smart manufacturing, intelligent methods for detecting and classifying machinery faults have been a key part of scientific research and interest. The fault detection system and the bearing health-state monitoring system are anticipated to provide information regarding the actual working state of the machinery equipment continuously without hampering the production line. Again, the mechanical vibration signals are considered rich sources of information for the appropriate analysis of processes related to bearing faults. These vibration signals can provide enough information about the location and type of the fault, which is helpful for fault diagnosis [1; 5]. The working process of the ball bearing system consists of sensors placed in different locations in the equipment, via which the signals are transferred to the data acquisition system for additional processing. Figure 2 shows the vibration data collection process of the CWRU ball bearing system. The performance of fault detection methods depends both on the quality of the vibration signals collected and on the efficiency of the applied signal processing and feature extraction methods [5; 6]. Traditionally, the maintenance of these REBs used to be a posterior task, usually taking place after the occurrence of the fault. Moreover, this kind of posterior maintenance procedure leads the machine to break down, resulting in financial loss and other casualties [3]. Hence, it is of great significance to surveil the bearing condition during the working state of the engine. Many signal processing, machine learning (ML), and deep learning (DL)-based methods have been suggested and implemented in bearing condition monitoring and bearing fault detection and diagnosis. Data-driven methods use signal processing techniques in the time domain, frequency domain, and time-frequency domain to analyze vibration signals. With the use of these signal processing approaches, the appropriate height of fault detection and diagnosis accuracies were stated [7; 8]. Nevertheless, these conventional signal processing methods carry some limitations. The time-domain method uses the natural properties of the vibration signals in the time domain, such as root mean square, crest factor, quadratic mean, and skewness. These characteristics can be used in dynamic system monitoring applications to effectively reflect transient machine conditions assuming a stationary signal. In actual industrial settings, condition monitoring signals are often complicated by time-varying environmental conditions such as temperature and lubricants. In addition to background  
Figure 1: CWRU bearing test rig system and components of REB. noise and interference, spectral changes and nonlinear behavior are also complicated. In other words, the actual vibration signal captured is not stationary, which limits the validity of time-domain statistics. Again, due to the weak amplitude and short duration of structural changes in the vibration signal in the initial stage, the frequency-domain approaches may be unreliable for evaluating non-stationary machine conditions. Another limitation of these methods is that they are inadequate to deal with non-stationary signals [9]. To be more specific, temporal analysis is not capable of finding the faulty component of the machine. The frequency peak of the bearing fault is not easily distinguishable through FFT analysis. Correspondingly, cepstrum analysis is computationally expensive, and it generates many undesired large peaks near the zero point, making the output complex to interpret. The prerequisite of some experience and knowledge regarding the resonance frequency and filtering band makes envelope analysis challenging to use.