# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 1 Introduction  
Rotating machinery is widely used in manufacturing, energy supply, rail transit, aerospace, and other industries and plays a fundamental role in industrial production [1]. As an important part of rotating machinery, bearings are a kind of mechanical part that is used to reduce friction between other moving parts. Rotating machinery performance can be significantly impacted by bearing failures, particularly under severe working conditions [2]. The incorrect handling of these failures can pose a serious threat to personal safety and have a major impact on social and economic development [3]. Therefore, to maintain a safe operating environment and ensure high production efficiency, it is crucial to accurately identify the health status of the bearings. The emergence of the industrial big data age has led to the collection and utilization of vast numbers of operational data in industrial production, which has accelerated the development of artificial intelligence techniques for fault diagnostics [4]. It has been difficult to meet the requirements of rapid and accurate diagnosis with traditional signal-based and model-based fault diagnosis methods, such as wavelet transform, state estimation methods, and so on [5, 6]. In order to automatically extract fault features and implement high-efficiency diagnosis, deep learning (DL)-based diagnosis methods have become research hotspots in recent years [7, 8]. Compared with traditional methods based on signal analysis and machine learning, DL-based diagnosis methods can reduce human experience interference and have more advantages in the field of intelligent diagnosis [9, 10]. In addition, deep learning has further evolved to encompass transfer learning, federated learning, meta-learning, and other advanced paradigms [11]. These developments have significantlycontributed to the enhancement of fault diagnosis methodologies, enabling the extraction of intricate fault features and the realization of high-efficiency diagnostic capabilities. Currently, the following presumptions underlie effective applications of DL-based methods: (1) The fault samples are drawn from the same distribution of data; (2) A significant quantity of excellently labeled samples with faults is needed. Nevertheless, it is challenging to meet these presumptions in actual industrial scenarios due to the following issues: (1) Samples acquired under various conditions will have different distributions due to variations in working conditions, equipment wear, and ambient noise. In the end, diagnostic performance deteriorates when a model trained under one situation is applied to another; (2) Only a small number of fault data are available under particular operating conditions, and it is challenging to gather substantial historical fault data in advance [12]. The use of intelligent defect diagnostic techniques in industry is restricted by these issues. Transfer learning (TL) aims to address the shortcomings of deep learning in fault diagnosis and offers a feasible and promising resolution [13]. TL allows models to apply knowledge learned from one task to another related task. As one of the important branches of TL, domain adaptation (DA) [14] transfers knowledge by narrowing the gap between two domains and learning domain-invariant features. In the application of bearing fault diagnosis, [PERSON] et al. [15] developed a multi-source dense adaptation countermeasure network suitable for bearing fault diagnosis through integrated fusion. [PERSON] et al. [16] constructed a shared feature generator network and double classifier network, added additional classifier differences to the adversarial domain adaption network, and achieved more effective cross-domain diagnosis in a domain distribution transfer scenario. [PERSON] et al. [17] built an adversarial transfer learning network to diagnose data pertaining to class imbalance.