## 1 Introduction  
In the process of industrial production, bearing failure is one of the common causes of mechanical equipment failure. Bearing status directly affects the operation of the whole rotating machinery; the existence of bearing failure will reduce the reliability and availability of equipment. Therefore, the timely diagnosis of bearing is of great significance in the current mechanical equipment maintenance and health management [1]. The traditional bearing fault diagnosis method mainly relies on manual experience and professional knowledge, but this method has some problems, such as time consumption, power consumption, and being easy to be affected by subjective factors. With the continuous progress of science and technology, the application of machine learning and artificial intelligence in various fields is more and more extensive, and has attracted wide attention. In recent years, with the continuous development of computer computing power, deep learning models with strong data-mining ability have been widely used in the field of fault diagnosis [2]. In the research of bearing fault diagnosis based on a machine learning model, [PERSON] et al. [3] used Wavelet Packet Transform (WPT) to decompose bearing vibration signals, and then used a Support vector machine (SVM) to carry out fault diagnosis. [PERSON] et al. [4] presentsa fault diagnosis method based on wavelet threshold (WT), variational mode decomposition (VMD), and random forest. [PERSON] et al. [5] extracted features from sound signals, reduced the dimensionality of features through Decision Trees to select important features, and then used a C4.5 Decision Tree algorithm to classify the selected features and complete fault diagnosis. In order to build models and process features, these machine learning methods all require certain prior knowledge and assumptions, and this method of manually extracting features has great limitations. Deep learning has strong feature-learning ability and does not need to rely on manual feature extraction, so it has received great attention and has been widely used in the field of fault diagnosis. [PERSON] et al. [6] applied Convolutional Neural Network (CNN) to the fault diagnosis of rolling bearing. [PERSON] et al. [7] proposed an adaptive one-dimensional CNN classification method which combined the feature extraction and classification blocks of the traditional mode into a special structure, learned the best features from the original vibration signals of bearing, and achieved a high degree of generalization. [PERSON] et al. [8] adopted the strategy of expanding a convolution kernel to obtain a larger receptive field, and the proposed model has good performance in terms of accuracy, noise resistance, and timeliness. For multi-channel data, [PERSON] et al. [9] proposed a method for fault diagnosis using dimensionality reduction in multi-line subspace and then used CNN to extract fault features. [PERSON] et al. [10] proposed a new bearing fault diagnosis algorithm based on the combination of Sparse Wavelet Decomposition and multi-scale one-dimensional CNN (1-DCNN), they used convolution terms of different sizes to extract signal features from multiple scales. The original vibration signal of bearing is a kind of time sequence signal, and the CNN model cannot process the time information in the signal, so the recurrent neural network (RNN) sensitive to the time sequence characteristics has been widely used in the field of bearing fault diagnosis. [PERSON] et al. [11] proposed a bearing fault diagnosis method based on RNN. A Long-Shot Term Memory (LSTM) network is a variant of an RNN that can solve the difficulty of RNN convergence and the disappearance of gradients. [PERSON] et al. [12] combined signal processing technology with LSTM network to realize fault diagnosis in the case of small sample size. In order to combine the ability of CNN to extract spatial features and the sensitivity of LSTM to time series information, [PERSON] et al.