## 1 Introduction  
High-speed trains usually operate at high speeds and high loads. Ensuring the safety of passengers' lives and property is the top priority for high-speed train operations. The key to this effort is to ensure the stable operation of high-speed train running section bearings. Since high-speed trains operate under changing working conditions, the bearings will be frequently affected by actual working conditions such as variable loads and variable speeds, resulting in damage to the stability of the bearings. Therefore, it is of great significance to conduct high-efficiency and high-accuracy fault diagnosis of high-speed rail bearings. The intelligent diagnosis methodology of deep learning (DL) has become an important alternative to traditional fault diagnosis methodologies due to its advantages such as high efficiency, high accuracy, and low professional expertise requirements [1; 2; 3]. However, intelligent diagnosis methods based on deep learning rely on a large number of accurately labeled datasets, and the prerequisites of independent and identical distribution also need to be satisfied by both training and testing samples [4]. In fact, damaged high-speed rail bearings are not allowed to operate under real working conditions [5]. There are obvious distribution discrepancies between the characteristics of a large number of laboratory bearing failure samples currently available and the characteristics of a small number of samples under actual operating conditions, as shown in Figure 1. A large number of experiments have shown that, when the transfer task is performed between laboratory bearing failure samples and actual operating samples, the DL method cannot achieve satisfactory accuracy [6]. Defining target domain loss through source domain loss is the basic idea of domain adaptation. The key to domain adaptation is to reduce distribution discrepancies between domains. This challenge is also called unsupervised domain adaptation (UDA) when the source domain is rich in labeled instances, contrasted with a paucity of unlabeled instances in the target domain [7]. The field of unsupervised domain adaptation mainly includes the following research directions: Statistics-based domain adaptation approaches focus on aligning data distributions and enhancing knowledge transfer between source and target domains. These methods include Max Mean Discrepancy (MMD) [8], Correlation Alignment (CORAL) [9], Wasserstein distance [10], etc. [PERSON] et al. introduced a Deep Adaptive Network (DAN) that employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the features of samples from the source and target domains across various layers in the Reproducing Kernel Hilbert Space (RKHS) [11]. This approach takes into account the global distribution of the features but may overlook the fine-grained information within the feature data, neglecting certain critical details in the distribution of the sample features. [PERSON] et al. presented the Deep Subdomain Adaptation Network (DSAN) [12], which utilizes the local maximum mean discrepancy (LMMD) to evaluate and address distribution discrepancies within the domain. By adjusting the feature extractor's parameters, subdomain-specific distributions can be aligned across different domains. Meanwhile, adversarial domain adaptation strategies implement a domain discriminator to foster confusion between domains, effectively minimizing relevant subdomain discrepancies. In the Domain Adversarial Neural Network (DANN), a domain discriminator is introduced to blur the boundaries between domain distinctions, while a gradient reversal layer (GRL) is positioned between the feature extraction and the domain discriminator. This setup is designed to enable the feature extractor to learn domain-invariant features during the backpropagation process [13].