## 1 Introduction  
High-power, heavy, and large-size industrial electric motors play a vital role in the production process at factories. Therefore, these electric motors should be strictly monitored using advanced equipment. When the fault status of these machines is diagnosed accurately, finding specific components causing the fault quickly can help factories reduce downtime and repair costs and maintain the quality of products. In common fault-diagnosis (FD) processes, the signals which contain the machine health status are acquired and transferred to the server system in which they are analyzed to diagnose faults. Although this approach is expensive compared with the use of handheld FD instruments using the diagnosis method for bearing faults [1], it can improve reliability, which is undeniable in machine monitoring, especially for essential machines. Bearing faults account for approximately 50% of faults related to electrical motors [2; 3; 4]. Therefore, many studies have focused on bearing FD using various types of signals acquired from sensors mounted on electric motors, especially vibration signals. In the last decade, with the advent of machine learning techniques, bearing FD methods using vibration signals have achieved high accuracy under specific conditions [5; 6; 7]. However, in realistic scenarios such as diagnosis of very early developed damage [3], diagnosis of very slow rotating machinery, and monitoring under high-vibration environments, acoustic emission (AE) signals can be more useful than vibration signals. Therefore, AE-based FD methods can be mainly considered in fault monitoring systems. Traditional digital signal processing-based FD methods such as envelope analysis achieve high accuracy only when the bearing speed is constant or changes slightly. Otherwise, when diagnosing bearing faults under variable rotational speed (e.g., in a wind-turbine system, the bearing speed continuously changes during operation [8]), acquired AE signals tend to be non-stationary. With the potential advantage of showing the change of signal frequency components with time, the time-frequency \((t,f)\) analysis showed its efficiency. Time-frequency \((t,f)\) representation can reveal more features of temporal localization of signal spectral components, resulting in better efficiency than frequency domain analysis and time-domain analysis. [PERSON] et al. proposed a method using continuous wavelet packet transform (CWT) to extract compressive waves that contain the consistent speed of a given frequency band to locate faulty planet gear in a wind turbine gearbox [9]. Moreover, the development of convolutional neural networks (CNN) has provided an automatic and incredibly efficient tool for extracting and selecting useful features containing fault information from time-frequency AE signal representations. Recent methods for compound bearing fault diagnosis under variable speeds used generic CNN architecture with three convolution layers based on Lenet-5 and enhanced the CNN training process by a stochastic diagonal Levenberg-Marquardt algorithm [10; 11]. In these methods, AE signals are converted into two-dimensional spectral energy distribution maps to feed the CNN. Those methods achieved high accuracy, but in noisy environments, this simple CNN architecture is hard to extract faults information effectively. With the higher diagnosis accuracy of CNN-based methods compared to traditional signal processing methods, applying a more suitable CNN model in bearing FD is essential to analyze the information from signals and then, enhance accuracy, consistency, and computation efficiency. In this paper, we propose a new method to achieve high FD accuracy in diagnosing multiple faults under various rotational speeds and noisy environments. In addition, the degradation level of each type of fault can be predicted in our proposed method.