## 1 Introduction
In an intelligent fault diagnosis approach that is based on migration learning, the data for model training and testing do not need to have the same probability distribution. This can be accomplished by exploiting the source domain with the help of technical methods of migration to accomplish the tasks in the target domain, especially for data samples where there are no or few markers in the target domain [17]. Recently, on the basis of various deep neural networks, scholars have proposed several migration learning methods for rotating machinery fault diagnosis, and experimental validation has been carried out on various types of datasets collected under different operating conditions [18; 19]. Domain adaptation is one of the migration learning methods that can be used to automatically align feature distributions in the source and target domains for deep feature extraction by using distribution difference metric functions, such as the maximum mean difference (MMD), multinomial kernel maximum mean difference (MK-MMD), and Wasserstein distance, which in turn realizes the migration from the source domain to the target domain to accomplish the task demands of the target domain. [PERSON] et al. [20] added the multiple maximum mean difference (MK-MMD) to a deep transfer network model to reduce the distributional difference of the data in the two domains and identified the faults of the target domains. [PERSON] et al. [21] utilized the multiple kernel maximum mean discrepancy to adjust the marginal distribution and the conditional distribution in the multidomain discriminators of the two domains to achieve extraction of the domain-invariable features and complete cross-domain fault identification. [PERSON] et al. [22] embedded the maximum mean difference into a fully connected layer in a convolutional neural network, thus enabling domain-invariant feature extraction to achieve pattern recognition of the faults. [PERSON] et al. [23] added an adversarial mechanism in combination with an autoencoder to achieve cross-domain diagnosis from experimental bearings to the actual wheelset bearings used in the test models. [PERSON] et al. [24] presented an improved domain-adversarial neural network with improved multi-feature fusion to achieve the classification of bearing health status. [PERSON] et al. [25] combined the adversarial mechanism and maximum mean discrepancy with a weight-sharing convolutional neural network to achieve fault identification under different operating conditions. In summary, there have been many researchers who have investigated deep migration learning methods from various perspectives to improve their performance for bearing fault state recognition. However, compared to conventional bearings, the forces on rolling bearings in high-speed trains are more complex and variable. Based on the conditions of variable loads and existing theories, a migration model for fault diagnosis, which incorporates an adversarial mechanism and a channel reconstruction unit (CRU), is established in this study. In the process of establishing the model, we analyze not only the influence of each module in the feature extractor on the performance of feature extraction but also the effect of the structure of the fault recognition module and domain discrimination module on the diagnosis accuracy. Finally, the method was experimentally analyzed and found to have good performance in identifying bearing fault states under different load conditions. The rest of the paper is organized as follows. Section 2 provides an introduction to the theory of domain-adversarial neural networks (DANNs), Section 3 describes the migration learning fault diagnosis framework, and Section 4 validates the performance of the method through experiments. Section 5 summarizes the paper.