## 1 Introduction  
The industrial motor is vital equipment in modern industrial application such as in pumps, air compressors, steel mills, paper mills, wind turbines, and others. It is perhaps the most commonly used rotating machine with the primary goal of providing manufacturing machinery rotation under different load conditions [1]. Thus, the bearing is the most critical and important piece of equipment in the industrial motor. However, under the continuous and heavy workload, rapid raising of the voltage pulse, and contamination of the operating environment, the metal fatigues of different parts of the bearing appear in the form of surface cracks [2]. The gradual increase of the crack severity becomes a major cause of failure of the bearing [2; 3]. Finally, the unattended bearing-related failures account for a significant portion of functional breakdowns in induction motors as well as shutdown of the industrial process. To overcome the situation and decrease the tremendous loss of industrial production, several types of bearing condition monitoring and early fault detection models have been invented. Conventional model-based condition monitoring is most popular among them. However, model-based condition monitoring demands a deep knowledge of the whole industrial process [4; 5]. Alternatively, the modern development of sensor technology, information processing techniques, and machine learning models help to invent an efficient data-driven bearing fault diagnosis model by considering a large amount of signal data during continuous industrial operations [4]. In the data-driven approach, data are collected from the actual system environment using different types of sensors for analyzing and distinguishing normal and abnormal conditions without developing the complex model of the system. To collect the data from the bearing, several types of sensor are used, i.e., current sensor, thermal sensor, vibration acceleration sensor, and acoustic emission sensor. Motor current signature analysis (MCSA) is adopted for fault diagnosis [6; 7] because of the fact that it is sensitive to different failures of industrial motor and provides nonintrusive monitoring. Vibration signal analysis-based fault diagnosis is widely used for this purpose, which may provide the most information about bearing failures [8; 9; 10]. The vibration analysis and MCSA show good performance for the early detection of bearing defects. However, these approaches can only show suboptimal performance in the modern complex industrial system because of their difficulty in capturing dynamic activities at weak vibration and current levels, respectively. Acoustic emission (AE) is a popular alternative method for fault diagnosis due to its high operating frequency range and low-energy signals capturing capability [4; 11; 12; 13]. AE can identify bearing abnormalities before they appear on the surface at the vibration acceleration range. Thus, AE is used to monitor for incipient bearing defects in this paper. In order to develop an efficient fault diagnosis technique, many researchers proposed the data-driven bearing fault diagnosis model. [PERSON] et al. proposes a heterogeneous feature extraction process using envelop power spectrum, time and frequency domain statistical analysis, and wavelet packet decomposition analysis. In this paper, the authors adopt a simple greedy search model, which is computational time expensive. The \(k\)-NN, feedforward net, and SVM (support vector machine) are used in a supervised manner in this research [14]. [PERSON] et al. proposed a hybrid feature selection algorithm and discriminant heterogeneous feature analysis for bearing fault diagnosis. In this paper, acoustic emission sensor is used for acquiring vibration data of the fault. A novel hybrid feature selection model is introduced for selecting optimal features from extracted heterogeneous feature vector. Finally, \(k\)-NN is used for identifying bearing fault [15]. In [16], [PERSON] et al.