# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 1 Introduction
Through adversarial training, they acquired information about class separation diagnosis for unbalanced data, and, through joint-distributed two-layer adversarial transfer learning, they acquired domain-invariant knowledge. While transfer-learning-based fault diagnosis techniques have shown promise, existing strategies primarily focus on minimizing distributional differences, overlooking variations in the underlying data structure of fault features. This oversight limits their effectiveness in capturing crucial structural information, thereby hindering performance in cross-domain fault diagnosis tasks. To address these limitations, a multi-perception graph convolution transfer network (MPGCTN) is proposed. Unlike previous methods, the MPGCTN not only aims to minimize distributional differences but also aims to capture the intricate data structures inherent in fault features, which is effective for variable-working-condition bearing fault diagnosis across different domains. Central to the MPGCTN is the multi-perception graph convolutional network (MPGCN), which leverages multiple receptive fields to extract robust feature representations from complex data relationships. By integrating class labels, domain labels, and data structures into its architecture, the MPGCTN facilitates high-performance domain adaptation by effectively modeling the inherent characteristics of fault data. Specifically, the MPGCTN comprises three key modules: a graph generation module, a graph perception module, and a domain discrimination module. In the graph generation module, a one-dimensional CNN (1-D CNN) is used to capture features from the input, and then the instance graph is constructed through the graph generation layer. Then, in the graph perception module, the proposed MPGCN is utilized to identify the fault features that are hidden in the relationship data between nodes with varying scales. Finally, in the domain discrimination module, adversarial training of the classifier and domain discriminator is conducted to reduce structural differences between target domain and source domain samples for cross-domain diagnosis. Twelve transmission fault diagnosis tasks are constructed using two types of bearing datasets with four different working conditions to verify the effectiveness of this method. The following are the primary contributions of the suggested method:  
(1) A multi-perception graph convolution transfer network (MPGCTN) is proposed which models class labels, domain labels, and data structure in deep networks and which achieves high-performance domain adaptation;(2) A multi-perception graph convolutional network (MPGCN) is proposed to obtain a more robust feature representation by aggregating features learned from multiple receptive fields;  
(3) The proposed method is evaluated on a cross-domain task of variable working conditions on two test platforms. Comparing the MPGCTN to other approaches, the results demonstrate its superior performance. The rest of this paper is structured as follows: Section 2 presents a concise overview of key concepts and theoretical foundations concerning graph convolution networks and domain adaptation. The method proposed in this study is thoroughly introduced in Section 3. In Section 4, a comparative analysis of the proposed model and a variety of other models is conducted using the CWRU and PU datasets. The paper's conclusion and future directions are covered in Section 5.