## 1 Introduction  
Rolling bearing is an important part of motor operation. Due to the variable load of the motor, the rolling bearing is more vulnerable to damage and failure [1], which affects the normal operation of the motor and causes serious economic losses. The motor bearing is more prone to collapse in the variable load operation stage than in the steady-state condition. Therefore, the fault diagnosis of the variable load rolling bearing is more practical [2]. However, for the fault diagnosis of motor bearing under varying load conditions, it is difficult to extract the characteristics of vibration signals under different loads due to noise pollution, resulting in a difficult fault diagnosis. Experts and scholars at home and abroad have carried out fruitful research on the fault feature extraction of rolling bearings. Wavelet transform [3] is widely used in bearing fault feature extraction because of its advantages in denoising. However, the decomposition of the high-frequency part of the signal by wavelet transform is not thorough enough, affecting the fault feature extraction ability [4]. [PERSON] et al. [5] propose an adaptive signal decomposition method, empirical mode decomposition (EMD). EMD can adaptively decompose the signal through a recursive algorithm. Still, the EMD decomposition of the signal is not thorough enough, and it is easy to produce modal aliasing, end effects, and other problems. Given the above problems, EEMD, CEEMD, CEEMDAN, and other methods have been proposed one after another [6; 7; 8]. In 2013, [PERSON] et al. [9] propose the variational mode decomposition (VMD) algorithm. Unlike the traditional recursive algorithm of EMD, VMD effectively avoids the phenomenon of modal aliasing by solving the modal components within the variational framework, and it has strict mathematical theoretical derivation [10]. Based on the above advantages, VMD has been widely used in fault feature extraction of bearing vibration signals. [PERSON] et al. [11] use the improved GWO to optimize VMD parameters and used it for signal decomposition. [PERSON] et al. [12] apply the maximum correlation kurtosis deconvolution (MCKD) algorithm to fault diagnosis. The MCKD algorithm can effectively enhance the fault information of rolling bearings by constructing and finding the optimal filter to highlight the continuous periodic pulse signal in the signal. [PERSON] et al. [13] use a method combining singular spectrum decomposition and the VMD and MCKD algorithms to analyze the spectrum of rolling bearings and adopted the Sparrow optimization algorithm to optimize MCKD's parameters. [PERSON] et al. [14], in their comprehensive tutorial, observe that most existing methods are validated under steady-state conditions and lack robustness in variable operational environments. [PERSON] et al. [15] demonstrate that parameter sensitivity and computational inefficiency remain critical challenges in the comparison of signal analysis techniques. Therefore, it is necessary to consider the different working condition problems of load machinery in the actual working environment, such as variable load and variable speed, when analyzing the faults of the inner ring, outer ring, or the rolling elements of rolling bearings. Many scholars have proposed different solutions to the fault diagnosis of rotating machinery under variable working conditions. [PERSON] et al. [16] propose a motor bearing condition identification method based on the enhanced symplectic geometric mode decomposition (ESGMD) and the self-organizing auto-encoder convolution network (SOAECN), solving the difficulties in the feature extraction and condition identification of vibration signals. However, it has limitations, such as the time-consuming self-organizing strategy, the need for optimization under unbalanced datasets, and the impact of the activation function selection on accuracy. [PERSON] et al.