# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 1 Introduction
Moreover, it achieved good performance for gearbox fault diagnosis under velocity changes. [PERSON] et al. [24] came up with a neural network named discrete-peak joint attention enhancement (DPJAE) convolutional model for unbalanced variable speed fault diagnosis. [PERSON] et al. [25] proposed a multi-objective instance weighting-based transfer learning network to solve the problem that the discrepancy between and within domains is large and successfully applied it to fault diagnosis. The deep domain adaptation model can get rid of the inherent disadvantages of the domain adaptation network with the help of a deep learning network and also maintain the advantages of the domain adaptation model, which can effectively solve the problem of data sample distribution discrepancy [26]. In recent years, a large number of researchers is focusing on adversarial learning represented by generative adversarial nets (GANs) [27]. Various deformation networks based on GANs appear one after another and have achieved good diagnosis effects [28; 29; 30; 31]. Compared  
Figure 1: The general frame diagram of domain adaptation. with the traditional deep neural network, adversarial learning networks have a great improvement in diagnosis; so, the idea of adversarial learning networks being appended in deep domain adaptation networks is also a hot topic. At present, some remarkable achievements have been attained based on the deep adversarial domain adaptation model. [PERSON] et al. [32] proposed an unsupervised domain adaptation based on maximum classifier discrepancy. [PERSON] et al. [33] introduced a deep transfer maximum classifier discrepancy method that, combined with a batch normalized long-short term memory (BNLSTM) model, successfully solved the problem under few labeled data. [PERSON] et al. [34] constructed an adversarial domain adaptation model by adding a domain discriminator and used deep coral to align target domain features with source domain features. Multi-group transfer experiments proved that this model achieved good diagnostic results. [PERSON] et al. [35] presented a deep convolutional transfer learning network (DCTLN), which contained two modules, condition recognition and domain adaptation. The two parts were promoted against each other, thus improving the diagnostic performance of the model to a certain extent. However, the above approach is considered from a single point of view, with only domain-level alignment or class-level alignment. Only domain-level alignment ignores the task-specific decision boundary and only aligns the characteristics of the two domains completely, ignoring the characteristics of each domain. On the contrary, only considering class-level alignment takes into account the characteristics of each domain, but may be limited by mismatched categories because it does not take advantage of global local knowledge. Consequently, this paper proposes a new fault method, namely, domain adaptation network with double adversarial mechanism (DAN-DAM). This method takes both domain-level alignment and class-level alignment into account and achieves satisfactory diagnostic results. Figure 2 roughly shows a classification effect comparison between the double adversarial domain adaptation method and other domain adaptation methods. The DAN-DAM model integrates domain discriminator and double label classifiers and uses a deep convolutional neural network (CNN) as feature extractor for feature extraction and the combination of pairs of them forms double antagonism. Specifically speaking, on the one hand, the feature extractor forms an adversarial mechanism with the two label classifiers to realize class-level alignment and Wasserstein distance [36] is used to measure the difference between the two classifiers. On the other hand, the feature extractor and the domain discriminator form another adversarial mechanism to realize the domain-level alignment and the gradient reversal layer (GRL) [37] is used to reverse the gradient automatically.