## 1 Introduction
The experimental platform is shown in Figure 1.  
The two PCB 352C33 unidirectional acceleration sensors in Figure 1 are fixed to the test bearing horizontally and vertically via magnetic bases. A DT9837 portable dynamic signal collector was used to collect the horizontal and vertical vibration signals from the  
Figure 1: Experimental platform (provided by the XJTU-SY dataset [5]). sensors. The experimental sampling frequency is 25.6 kHz, the sampling interval is 1 min, the sampling duration is 1.28 s, and the data sampled each time are 32,768 time-series vibration signals. Data preprocessing includes signal processing and dimension transformation. For example, [PERSON] et al. [10] studied the effect of data preprocessing methods and super parameters on rolling bearing fault detection accuracy in deep learning. The higher diagnostic accuracy of the 2D image data format of the convolutional neural network was confirmed by one-dimensional and two-dimensional conversion of the data. [PERSON] et al. [11] proposed a combined transfer learning method, which uses continuous wavelet transform to construct the original vibration signal into time-frequency images, and constructed a self-attention light convolution neural network model. The experimental results verify the effectiveness of the transfer learning method. Compared with other regular CNN models, the classification accuracy of this method reaches 99.5% when there are fewer training samples. More importantly, this shows that the transfer learning method has high accuracy while staying lightweight. Although the accuracy of bearing fault diagnosis can be improved by converting the input data of two-dimensional images, the input of image data will greatly increase the network training time. These continuously improved time-frequency domain-based fault diagnosis methods have been able to extract the fault features in vibration signals well, but these methods require specialized background knowledge and complex signal processing to achieve better diagnostic results, and applying them in complex environments and with large amounts of data would take considerable effort. Deep learning network model  
Traditional machine learning methods such as artificial neural networks (ANNs) [12] and support vector machines (SVMs) [13] have been better applied in bearing fault diagnosis. ANN learns using training mechanical fault information and diagnosis experience, and then expresses the learned fault diagnosis knowledge using connection weights distributed inside the network. SVM is a generalized linear classifier for binary classification of data using supervised learning, which transforms non-separable low-dimensional data into separable high-dimensional data and establishes the optimal separation hyperplane based on kernel functions to satisfy the classification. Compared with the methods based on the application of signal processing alone, the methods based on machine learning have better adaptability and performance. However, early machine learning methods are highly dependent on expert knowledge and manual feature selection, and these shallow machine learning methods have limited representation capability and cannot make full use of massive data, and the bearing fault diagnosis under complex operating conditions still needs to be improved. In 2006, [PERSON] et al. [14] put forward deep learning for the first time, opening the door to the scientific research field of deep learning. The convolution neural network (CNN) is the most representative algorithm of deep learning technology, and the deep learning network model built using the CNN is also the direction that many scholars have continued to explore [15; 16; 17; 18; 19]. In 2012 at the ImageNet competition, [PERSON] et al. [20] proposed the AlexNet large convolutional neural network. This network reduced the top-5 error rate to 15.3% and began the boom in deep learning techniques.