## 1 Introduction
Since the EMD method has an excellent processing effect on non-stationary feature information, it is often used in time-frequency domain signal analysis [7]. Some scholars have made further improvements to the EMD method by adding white noise to the vibration sequence to overcome the shortcomings of modal aliasing. This method is called ensemble empirical mode decomposition [8]. [PERSON] et al. [9] converted the vibration signal into a fast entropy fault characteristic map, which highlights the periodic fault pulse characteristics and improves the model's anti-noise ability and stability. However, this method has certain limitations, and it is not obvious to the random vibration pulse. In the power distribution system, it is difficult to extract single-line ground fault characteristics and a diagnosis of fault. To solve this problem, [PERSON] et al. [10] used the auto-encoder and fuzzy C-means clustering method to realize fault location and used the fuzzy C-means clustering method without threshold to perform feature classification to realize the fault diagnosis of a resonance grounding power distribution system. To effectively extract the features of non-stationary signals in heavy noise, [PERSON] et al. [11] proposed an improved fault diagnosis framework for variational modal decomposition and signal reporting, using the peak value and the maximum value of the correlation coefficient to calculate the best intrinsic mode function (IMF). Then, the IMF is reconstructed into a noise-reduction signal, and the early fault characteristics are accurately extracted. [PERSON] et al. [12] proposed a network framework for fault diagnosis based on local weighted multi-conditions and multi-labels, which uses the information contained in the encoder to realize fault diagnosis. [PERSON] et al. [13] proposed a multi-condition bearing fault pattern recognition framework, which uses compound multi-scale fuzzy entropy to efficiently extract fault feature information, and added a whale optimization algorithm to improve the training speed of support vector machine models and improve the fault pattern recognition rate. In the third stage of development, with the iterative update of computer hardware, its ability to deal with complex problems has become stronger. Deep learning frameworkssuch as long- and short-term memory (LSTM) networks, fuzzy inference, support vector machines, decision trees, and convolutional neural networks have been implemented [14]. With the rapid development of machine learning algorithms and information system technology, intelligent diagnosis methods have also begun to be widely used in the field of rolling bearings. Intelligent diagnosis is based on different deep learning algorithms to preprocess vibration signals and extract deep features to achieve fault diagnosis [15]. [PERSON] et al. [16] extracted the features through the variational modal decomposition of the original time series of the rolling bearing and then used the generalized regression network model to learn the feature information, train the model parameters, and use the maximum value normalization to achieve fault classification. The size of the traditional convolutional neural network convolution kernel is very small, and it ignores the detection and extraction of the overall information of the fault information feature distribution. [PERSON] et al. [17] designed a one-dimensional visual convolutional neural network which uses a multi-layer small convolution kernel and a single-layer large convolution kernel to form a convolutional layer side by side. While the model improves the stability of the training process, it also improves the accuracy of the model's recognition of rolling bearing faults. Since the automatic encoder has a good performance in feature extraction and size reduction, it is often used for fault diagnosis of rotating machinery [18]. [PERSON] et al. [19] proposed restricted sparse networks based on the quadratic complex domain equation. [PERSON] et al.