# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 4 Experimental Setup and Results  
### Open Datasets  
To verify the validity of the DAN-DAM model, the open datasets provided by Case Western Reserve University are first used for experimental verification [40] and the experimental platform is shown in Figure 4. The experiment platform is mainly composed of four parts: motor, drive end bearing, torque sensor and dynamometer. The driving end bearing model is an SKF6205 deep groove ball bearing and 12 K driving end bearing fault data are adopted. The fault types of bearing are divided into three categories, namely, roller fault, inner race fault and outer race fault. At the same time, the data of each fault type contain three fault types with different degrees of depth: shallow (7 mil), moderate (14 mil) and severe (21 mil). Therefore, there are altogether 9 fault types of bearing, while, plus normal bearings, there are altogether 10 types of bearing data, as shown in Table 2. Fourier transform frequency domain data are used in the transfer experiment, with 1000 data per bearing type and 600 lengths per data. Four speed types with different intervals are adopted, namely, 1730 r/min, 1750 r/min, 1772 r/min and 1797 r/min, which are represented by SP1, SP2, SP3 and SP4, successively. A total of 12 groups of different  
the highest diagnostic accuracy and the lowest diagnostic accuracy is 11.95%, indicating relatively poor stability. Although the EAFCNN model has achieved good diagnostic results in most cases of transfer, there are several groups of poor diagnostic accuracy, lower than 87%, and the diagnostic accuracy fluctuates greatly, with the maximum difference being 15.85%. The diagnostic effect of WDCNN is the worst and the highest diagnostic accuracy is 91.01% under the transfer of each group, which is 5.32% different from the lowest diagnostic accuracy of the DAN-DAM model, and the stability is also lower than that of DAN-DAM model. In order to further elaborate the stability of the proposed method, we draw the test results of the DAN-DAM model in the target domain for five consecutive times under each transfer condition, as shown in Figure 5. According to the analyses in Figure 5, on the one hand, the model maintains a high diagnostic accuracy in each transfer situation and most of the experimental results maintain an average of about 98%. On the other hand, looking at the output results of the same set of transfer situations for five consecutive times, the accuracy curve fluctuates relatively little and the basic fluctuation remains within 4%, so the model diagnosis effect is relatively stable. MMD is added to the feature extraction stage of the DAN-DAM model. The main function of MMD is to align the features of the source domain and the target domain in the high-dimensional space, so that to the samples in the unlabeled target domain are gradually closer to the samples in the labeled source domain. To verify the above functions of MMD, the characteristics of output layer of the DAN-DAM model and the DAN-DAM (no MMD) model are visualized by t-SNE. SP3- > SP4 is taken as an example and the comparison of visualization results in the output layer of the two models is shown in Figure 6. As can be seen from Figure 6, the classification performance (diagnostic performance) of the DAN-DAM model is generally greater than that of the DAN-DAM (no MMD) model. The specific analysis is as follows: On the one hand, from the perspective of the classification of various types of samples, the DAN-DAM model and the DAN-DAM (no MMD) overall can distinguish various types of samples, but, with the DAN-DAM (no MMD) model, relatively many samples are mistakenly classified into other types of samples, comparing to the DAN-DAM model. For example, in Figure 5(b), C3 and C4 in part overlap and, at this time, C3 is misjudged as C4. On the other hand, from the clustering effect of various types of samples, the DAN-DAM model has a better clustering effect than the DAN-DAM (no MMD) model.