## 1 Introduction  
With the progress of science and technology, mechanical equipment has become more complicated and sophisticated. As a result of mechanical equipment playing an important role in actual production, the production process is seriously affected when a fault occurs. Consequently, it is very meaningful to carry out health monitoring and fault diagnosis. According to statistics, 41% of induction motor failures result from bearing faults [1]. Therefore, research on motor bearing fault diagnosis is particularly significant. Previous research on bearing fault diagnosis has mostly focused on vibration signal acquisition and feature extraction. For example, [PERSON] et al. [2] provided a comprehensive and systematic summary of recent research for fault diagnosis of rolling bearings using vibration signals. On the one hand, adding vibration sensors obviously increases costs in actual production. On the other hand, installing sensors on high-precision equipment is often impossible, especially under some special working conditions. Therefore, detection of bearing faults through the signals obtained from the equipment itself has received widespread attention, such as the motor current signal (MCS) [3; 4; 5], which can be directly measured by a frequency converter without installing other sensors. Meanwhile, due to the rapid development of deep learning and its excellent performance in terms of fault diagnosis technology, research on bearing fault diagnosis based on the combination of deep learning intelligent algorithms and current signals has remarkable engineering value [6; 7]. Compared with vibration signal analysis, motor current signature analysis (MCSA) is a relatively new method for the fault diagnosis of rolling bearings [8]. Owing to the benefits of energy savings and soft motor start-up capability, variable frequency drives (VFDs) have been extensively applied in mechanical equipment [9]. Nevertheless, VFD output harmonics result in a poor signal-to-noise ratio, which makes the application of stator current signals challenging [10]. According to the above current signal characteristics, some scholars have carried out much research on signal processing. Currently, bearing fault diagnosis mainly centers on time-domain, frequency-domain, and time-frequency-domain analysis. In particular, spectral analysis is the most widely used method. First, time-domain analysis is the most straightforward technique for fault detection and diagnosis, which usually involves the scalar indices used to determine bearing conditions [11]. Time-domain analysis extracts fault features by calculating statistical parameters such as the kurtosis, crest factor, skewness, and probability density curve [12]. Due to the gradual increase in the degree of bearing faults over time, [PERSON] et al. [13] monitored the amplitude of current signals in the time domain. In addition, some research has attempted to combine the time-domain and frequency-domain features of the stator current signal to extract fault features for bearing fault diagnosis [14; 15]. Due to the weak time-domain features of the current signal and the minimal relationship between them and bearing faults, time-domain features are often used as auxiliary analysis combined with frequency-domain analysis. Second, spectral analysis of the current signal is the most widely used method at present. Nevertheless, most studies based on current signals are devoted to motor fault diagnosis, while studies on bearing fault diagnosis are relatively scarce. For example, to make frequency analysis more effective, a normalized frequency-domain energy operator is proposed to avoid the supply frequency masking the fault frequency component for broken rotor bar fault diagnosis [16]. Similarly, [PERSON] et al. [17] used a third-order energy operator for current signal demodulation to enhance the fault features. [PERSON] and [PERSON] [18] observed the Hilbert envelope spectrum of a current signal and performed spectral analysis to detect fault-related harmonic components.