# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 2 Preliminary Information  
### Related Works  
In industrial applications, bearings are key rotating machinery components, and monitoring and fault diagnosis of their condition is crucial to ensuring the stable operation of equipment. Especially under changing working conditions, traditional fault diagnosis methods are often difficult to adapt to different operating environments, resulting in a decline in diagnostic performance. In recent years, deep learning, as a powerful end-to-end strategy, has been widely used to diagnose bearing faults intelligently. This section reviews significant recent research progress in the intelligent diagnosis of bearing faults under variable operating conditions.  
In recent years, multi-scale feature learning has shown strong potential in bearing fault diagnosis. For example, [PERSON] et al. [44] proposed a method based on multi-scale convolutional neural networks to effectively extract bearing fault features for fault diagnosis. [PERSON] et al. [45] proposed a novel fault diagnosis method based on enhanced multi-scale sample entropies and balanced adaptation regularization for fault diagnosis needs under different working conditions. To solve the problem of inconsistent training and test data distribution, [PERSON] et al. [46] explored the application of transfer learning technology in bearing fault diagnosis. They proved its effectiveness in cross-domain fault diagnosis. [PERSON] et al. [47] employed transfer learning and random forest classification to exemplify the potential of combining deep learning techniques with traditional algorithms in bearing fault diagnosis. Moreover, researchers have also widely discussed data enhancement methods based on generative adversarial networks (GANs). [PERSON] et al. [48] developed a data augmentation strategy based on GANs to improve the model's generalization ability under varying working conditions. [PERSON] et al. [49] encapsulated a shared feature extractor, a label predictor and a series of domain discriminators to propose an adversarial multi-source data subdomain adaptation (AMDSA) model. [PERSON] et al. [50] present a deep subdomain adaptation network (DSAN) by aligning the relevant subdomain distributions of domain-specific layer activations across domains based on a local maximum mean. [PERSON] et al. [51] explore the application of graph neural networks in bearing fault diagnosis.  
In addition, ensemble learning methods improve diagnostic accuracy by combining the prediction results of multiple learners and have also received increasing attention. [PERSON] et al. [52] proposed a multi-sensor information fusion method based on ensemble learning to automatically learn fault-related information from multi-sensor signals and provide accurate diagnosis results. [PERSON] et al. [53] proposed an explainable learning framework to further improve the accuracy and robustness of bearing fault diagnosis under variable working conditions.  
Through these studies, one can see the rapid development of intelligent diagnostic technology in bearing fault detection. These techniques improve the accuracy of fault diagnosis and enhance the model's adaptability in the face of changing operating conditions.