# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 1 Introduction  
In the process of operation, rotating machinery equipment is often subjected to sudden load increase and load reduction, resulting in stress and speed change of rotating machinery equipment [1]. These sudden load changes and sudden speed changes easily cause damage to key rotating parts. If they are not found and handled in time, the overall mechanical performance is directly and seriously affected and certain economic losses and even casualties may be caused [2; 3; 4]. Hence, without affecting the normal operation of mechanical equipment, in the early stage of failure, accurate detection and diagnosis of its health condition is of great significance [5; 6]. Over the years, a large number of advanced fault diagnosis methods, such as signal processing [7] and machine learning [8; 9], have been proposed successively, which have achieved good results in the field of mechanical fault diagnosis [10; 11; 12]. However, the above methods more or less rely on expert knowledge and waste of manpower and intelligence is poor. In recent years, the rapid development of deep learning has injected new vitality into fault diagnosis and achieved impressive results. For example, [PERSON] et al. [13] proposed an intelligent fault diagnosis method for rotating machinery based on local binary convolutional neural networks. [PERSON] et al. [14] presented an intelligent fault diagnosis method for rotating machinery with local and non-local information based on a semi-supervised deep sparse auto-encoder (SSDSAE). [PERSON] et al. [15] constructed a novel fault diagnosis method using deep variant sparse filter networks (DVSFN). [PERSON] et al. [16]proposed an internal parameter optimized deep belief network (DBN) method based on the grasshopper optimization algorithm (GOA). [PERSON] et al. [17] introduced an intelligent fault diagnosis method based on a one-dimensional convolutional neural network (1D-CNN), aiming at the problem of a small number of fault samples. [PERSON] et al. [18] presented a convolutional neural network-based data-driven intelligent fault diagnosis technique for rotary machinery which uses a model with optimized hyper-parameters and network structure. Although deep learning has made remarkable achievements in the field of fault diagnosis, careful study shows that these methods are all based on the premise that training data and test data belong to the same distribution. However, this situation does not exist in a real state in which the sample distribution of training data is different from that of test data. Thus, the trained model (the distribution of training data and test data is the same) is not suitable under this condition and the diagnosis effect is poor or even invalid. Therefore, for the sake of solving the problem of inconsistent data distribution, the domain adaptation [19] comes into being. The general frame diagram is shown in Figure 1.  
As the most commonly used transfer learning method at present, domain adaptation does not require that training data and test data have the same distribution. This method successfully solves the dilemma faced by the current mechanical fault diagnosis field [20]. However, complex problems cannot be solved effectively only by relying on the domain adaptation model, which is due to the model established on the basis of a shallow learning model, so as to limit learning ability. With the rapid development of deep learning, more and more researchers combine domain adaptation with deep learning to study the theory of deep domain adaptation learning and establish the diagnosis model of deep domain adaptation. [PERSON] et al. [21] developed a new deep neural network model with domain adaptation for fault diagnosis. [PERSON] et al. [22] introduced a method that extracts machine-invariant features using a deep auto-encoder and aligns the extracted features using domain adaptation to achieve cross-machine fault diagnosis. [PERSON] et al. [23] raised a novel domain adaptation method based on deep learning.