# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 1 Introduction
These studies have improved the correct rate of fault diagnosis. Although traditional machine-learning methods can be trained automatically, features still need to be extracted manually, which may have the problem of incomplete feature selection and slow speed. Deep learning has solved this shortcoming completely and has become the most popular tool in the domain of fault diagnosis. The first one to detect bearing faults using convolutional neural networks (CNN) was published in 2016 [13], and since then, papers using CNN for bearing fault diagnosis have continued to appear. A lot of work has been performed in the domain of fault diagnosis, which provides new ideas for using CNN for fault diagnosis [14; 15; 16]. A recurrent neural network (RNN) is suitable for processing sequence data, so it can be used for fault diagnosis. In 2015, [PERSON] et al. applied recurrent neural networks to fault diagnosis [17]. In their paper, firstly, a discrete wavelet transform was used to extract features, then the features were analyzed and selected, and finally, an RNN was used for classification. [PERSON] and [PERSON] proposed an adversarial fault diagnosis method based on confrontation [18]. In the model, an adversarial network was used to balance unbalanced signals. Experiments show that the classification performance of this method is better than other data balance methods in the case of imbalanced data sets. Domain generalization belongs to transfer learning, which is a data-based method that mainly maps data belonging to different domains using a specific method, calculates the difference in distribution between data in different domains after mapping, and reduces the difference between domains after mapping by training the mapping method so as to find a mapping method that minimizes the difference in the distribution of different domains to achieve domain generalization. The domain generalization problem was first introduced as a machine-learning problem by [PERSON] et al. to solve cell classification problems in medicine [19] and has since been applied to transfer models between multiple domains. [PERSON] et al. used a complex nonlinear neural network to minimize the variance between the source and target domain features [20], and [PERSON] et al. made the source and target domain feature distributions more similar by minimizing the MMD distance between the source and target domain feature distributions and using adversarial learning [21]. Without using distance methods, [PERSON] et al. achieved the domain generalization task using minimized contrastive loss [22]. In autopilot scenarios, the model does not change according to real-world domain changes, which hinders the generalization of object detection across different real-world domains, and normalization perturbations are proposed to cope with classification domain generalization [23]. [PERSON] et al. proposed a temporal domain generalization with drift-aware dynamic neural network to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change [24]. In order to achieve the goal of domain generalization, this paper introduces a special scenario for domain generalization called Load-Domain(LD) domain generalization and proposes a feature shift model (FSN) for it. LD domain means domains classified by their load (an attribution in the CWRU bearing dataset), so the domain labels have some physical information (actual horsepower of the bearing) we can use. FSN is designed for this scenario, where features are shifted from the target domain into the source domain to achieve domain generalization. In addition, the fully connected classifier and the relational classifier enhance the generalization ability of the feature extractor of FSN and achieve high accuracy on unseen target domains.