## 1 Introduction  
China has the longest and most complex high-speed rail system in the world [1]. The advantages of rail traffic, such as high economy and transport prices, contribute to higher demand and increased traffic intensity, as well as to higher loads and speeds of trains, which ultimately results in the rapid deterioration of this infrastructure [2, 3]. High-speed trains (HSTs) are often considered one of the best modes of transportation, and China has the longest and most complex high-speed railroad system, with HSTs operating at a maximum speed of up to 380 km/h [4]. At such high speeds, bearings, as important parts of the transmission system, are subjected to complex alternating loads, which increase the wear rate of the bearings and reduce their service life [5]. They are an important part of HSTs, and their health situation is greatly related to the operation of the whole train. The majority of the existing research on bearing fault diagnosis primarily focuses on the vibration signals generated by bearing impacts, which contain information concerning the healthy situation of the bearings. Analysis of the vibration signals and the information concerning the healthy situation of the bearings is crucial for the diagnosis of bearing faults in high-speed trains. With the proposition of machine learning methods, more and more academics are studying the correlated intelligent diagnosis algorithms and have successfully produced a variety of effective fault diagnosis algorithms based on vibration acceleration signals. Conventional intelligent diagnostic approaches have often used a mixture of signal processing measures and classification methods, using signal processing measures that have a well-established theoretical foundation, such as variational modal decomposition (VMD) [6], empirical modal decomposition (EMD) [7], and empirical wavelet transform (EWT) [8], to obtain signal features. These features are then input into classification methods, such as support vector machines (SVMs), extreme learning machines(ELMs) [9], and back-propagation networks (BPNNs) [10], to recognize the bearing fault state. Although these methods can classify and identify the bearing fault state, the accuracy of its diagnosis is strongly linked to the fault features extracted by the conventional signal processing methods in the previous period. Feature extraction using traditional signal processing methods mainly relies on personal experience, and it is difficult to sufficiently extract the correlated information from the vibration signal. This results in low-quality feature extraction, thus affecting the accuracy of the final fault identification. [PERSON] et al. [11] combined the spatial discard regularization method with separated convolution to extract features from the original signals, achieving effective differentiation of the original signals of bearings in different states. [PERSON] et al. [12] used improved one-dimensional and two-dimensional convolutional neural networks to achieve intelligent recognition of the bearing states, demonstrating high recognition accuracy. [PERSON] et al. [13] combined a deep belief network and convolutional neural network to extract relevant information from grayscale images and time series signals and used the fused deep learning model to recognize the fault types. More adaptable intelligent fault diagnosis approaches based on deep learning [14; 15; 16] can be used to feature vibration data automatically. However, their excellent performance depends on massive amounts of well-labeled training data and requires that the training data and the test data fulfill the condition of being identically distributed. In practical engineering work, owing to the effects of complex working environments, it is difficult to obtain enough data with markers of the same distribution, resulting in the inability to effectively apply all kinds of intelligent fault diagnosis measures based on deep learning.