## 1 Introduction
[16] proposed solving the data imbalance problem by using a convolutional neural network with a minimum-maximization objective function. Next, [PERSON] et al. [17] introduced the \(1\times 1\) convolution kernel, replacing the fully connected layer of the traditional convolutional neural network with global average pooling, aiming to reduce the model training parameters. Although CNN has achieved good results, it faces the following two problems in the field of bearing fault diagnosis: 1 The maximum pooling or average pooling used by CNN directly merges the information, which leads to the key information being unable to be identified. 2 It must be satisfied that the training set and the test set have the same probability distribution, but it is difficult to meet this assumption because of the complex and changeable working conditions in practical engineering. When the working condition of the equipment changes greatly, the recognition effect of CNN model will decrease significantly. To solve problem 1, a split attention module is introduced into the network, and a multi-channel structure and attention mechanism are adopted to enrich the diversity of fault features, strengthen the connection between fault features, improve the network's learning of fault features, and avoid the loss of key fault features. To solve problem 2, some scholars introduce the idea of transfer learning (TL) [18]. TL can solve the problem of cross-domain distribution difference and is widely used in the field of fault diagnosis. For instance, [PERSON] et al. [19] used polynomial kernel-induced distance to measure and evaluate the distributional difference between the source and target domains. [PERSON] et al. [20] used enhanced transfer convolution network to solve the decision boundary confusion problem in two domains. Further, [PERSON] et al. [21] introduced the adversarial idea and trained the classifier to confuse the sample features of the two domains; this was carried out to align the domains. However, such transfer learning methods only consider the distribution differences of the whole domain, not the distribution differences of related subdomains. As shown in Figure 1a, intra-domain data chaos will occur after global adaptation. Similar characteristics of fault samples may be wrongly classified. Aiming to mitigate said deficiencies, the authors proposed a bearing fault diagnosis model based on the deep subdomain adaptive split attention network (SPDSAN). This method extracts the signal features through the residual network fused with multipath and channel attention mechanisms. Next, local maximum mean discrepancy (LMMD) is used to align distributions of related subdomains in two domains--as shown in Figure 1b. The scientific contributions of this paper are as follows:  
1. The vibration signal was transformed into a time-frequency graph by continuous wavelet transform as the learning object of the network. Compared with the one-dimensional vibration signal, the time-frequency graph can not only provide the time-domain and frequency-domain characteristics of the fault, but also avoid the network to learn the single dimensional characteristics and affect the diagnosis accuracy. 2. The split attention module was introduced into the feature extraction network, and the multi-channel structure and attention mechanism were adopted to enrich the feature map diversity and improve the ability of the network to learn fault features. 3. LMMD was used to measure the difference of relevant subdomains in the source domain and target domain data, and the distribution of relevant subdomains under the same category was adjusted to capture the fine-grained information of each category, so as to achieve the subdomain alignment. 4. The method performance was compared to several widely used intelligent bearing fault diagnosis methods, and its effectiveness was verified.