## 2 Related Works  
### Research on Intelligent Data-Driven Fault Diagnosis  
The related literature about applications of fault diagnosis is summarized in Table 1, which is divided into three categories in terms of detection technologies such as deep learning, maximum mean discrepancy (MMD), and domain adversarial learning. Data-driven health condition detection approaches are reaching remarkable strengths in handling machinery health condition signaling [19; 20; 21]. In particular, deep learning has attracted increasing attention in related research; thus, the basic fault diagnosis problems that arise from source data and target domain data obeying the same distribution have been well solved [22; 23]. Meanwhile, due to increasing demand for generalization ability of monitor and diagnosis models in industry, the transfer learning has been widely used to shrink distribution variance of the feature representations [24]. Currently, most of the domain shift problem has been handled using domain adaptation methods [25], and shallow domain-invariant features are extracted by optimizing the distribution network [26; 27]. [PERSON] et al. [28] proposed a novel transfer diagnosis approach to sparse target data to minimize distribution discrepancy and address the issue of label space mismatching. A deep convolutional neural-based diagnosis methodology was designed by [PERSON] et al. [29] to monitor health conditions and optimize the data distribution of the ball screw. [PERSON] et al. [30] considered the discriminative feature learning in network, and realized fault diagnosis by building a deep discriminative transfer learning network (DDTLN). [PERSON] et al. [31] proposed a new transfer alignment method to solve the problem of the large data differences across domains. However, in the current literature, research on domain adaptation detection with insufficient source domain labels is still limited. In addition, the distance measurement is also an important issue of transfer learning. Among many distance measures, the MMD is one of the most widely applied distance metrics in transfer learning, which is to find difference between two probability distributions by mapping the data to another space [32; 33; 34]. By optimizing the MMD metric, the domain distance can be minimized, and the extraction of generalized knowledge can also be promoted. [PERSON] et al. [35] proposed a novel domain adaptation model based on a deep neural network for fault diagnosis, which realized the diagnosis by minimizing MMD and enhancing representative features. [PERSON] et al. [36] proposed a sparse filtering-based domain adaptation (SFDA) model to obtain high-dimensional features in cross-domain diagnosis tasks, in which l1 norm and l2 norm were implemented on MMD. And a cross-senor domain adaptation approach is proposed by [PERSON] et al. [37] to achieve indirect measurement of sensors and diminish the MMD of high-level representations. In addition to the popular MMD metric, the domain adversarial network developed from the generated adversarial network (GAN) has also become an effective diagnosis method because of its powerful capability to grasp domain generalization features [38; 39]. [PERSON] et al. [40] added an adversarial mechanism to the training of neural network firstly, and called the resulting network a domain adversarial neural network (DANN). Moreover, the domain adversarial network demonstrated excellent performance in fault diagnosis in recent years. [PERSON] et al. [41] presented a hybrid fault diagnosis model based on data augmentation generative adversarial networks (DAGAN) and DANN, wherein they effectively utilized a small amount of sample data to realize fault diagnosis under different working conditions. The novel deep subdomain adaptation graph convolution neural network was proposed by [PERSON] et al. [42], and the adversarial network and structured subdomain adaptation were adopted for reducing the distribution differences. In fields of industrial applications, [PERSON] et al.