## 1 Introduction
Aiming to address the problem that the supply frequency component of the current signal is dominant and the fault sideband amplitude is too low to detect, some frequency spectrum preprocessing methods based on the stator current are applied to motor fault diagnosis [19; 20; 21]. In the Fourier spectrum, the sideband appears near the generation frequency, and the fault characteristic frequency is within its interval. In addition, the amplitude modulation (AM) and frequency modulation (FM) characteristics of the analytic signal model enable us to use demodulation analysis to avoid the difficulties caused by complex sideband analysis in the Fourier spectrum [22]. However, the current signal is severely modulated by several factors during actual operation, such as the power frequency and bearing rotation frequency. Its application in engineering is still limited due to the complexity of modulation and demodulation in signal processing. Recently, to observe a more subtle time-varying process of the signal, time-frequency analysis has been fully developed in recent years because it can express a signal in the time domain and frequency domain synchronously. A bearing fault leads to rotor radial motion and load torque changes; the former adds additional frequency to the stator current, while the latter causes phase modulation of the stator current, which results in a time-varying frequency content [23]. [PERSON] et al. [24] detected the rotor eccentricity caused by bearing faults by collecting three-phase current signals from the motor, and the results show that advanced transient current signature analysis (ATCSA) has good industrial application potential. The continuous wavelet transform (CWT) was used to effectively extract the features of fault components from the current signal, and the 2D and 3D wavelet scalograms were used to achieve time-frequency characterization [25]. By means of conducting multiresolution analysis on square current signals based on the maximum overlap discrete wavelet transformer (MODWT), the extracted time-frequency features were used for bearing fault diagnosis [26; 27]. However, similar to the case of frequency-domain analysis, due to the complex modulation of the frequency component of the current signal, it is still not well processed even in the time-frequency domain. Meanwhile, many of these methods are only adapted to specific failure conditions, so it is necessary to correctly evaluate the accuracy and applicability of the developed method for current signals under different fault conditions. To avoid the complex feature extraction process of signals, deep learning (DL) and neural networks have received much attention in fault diagnosis due to their model independence and superior performance, which are properties of data-driven approaches [28]. For instance, [PERSON] and [PERSON] [29] combined variational mode decomposition (VMD) and an artificial neural network (ANN) to perform the fault diagnosis of rolling bearings. [PERSON] et al. [30] added an attention layer to the bidirectional long-short-term memory (LSTM) neural network model to obtain degradation features of three different patterns of the brushless DC (BLDC) motor and make future predictions. [PERSON] and [PERSON] [31] avoided information loss during the process of converting into images using mathematical models by converting the three-phase stator current into a voxelated 3D data structure, and then fault diagnosis is performed by inputting them into a 3D CNN model. Many studies have applied convolutional neural network (CNNs) and their variants to the fault diagnosis of electric machines and achieved good results [32; 33; 34]. However, due to the limitation of the size of the convolution kernel and its convolution operation mode, capturing long-range feature information using the above methods based on CNNs and their variants is very difficult [35].