# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 4 Experiment Result Analysis
### Outcomes of Maximum Classifier Discrepancy  
The MCD model also is selected for testing this improved feature extractor. For training the MCD, 0 indicates the category of source data, and 1 indicates the category of target data. Furthermore, this paper has applied pseudo-label semi-supervised learning in MCD. The following line chart represents the testing accuracies and discrepancy loss of MCD and MCD with the pseudo label in 100 epochs. The details are shown below. Figure 9 gives information about the accuracies of MCD and MCD with the pseudo label. In the first chart, the changes in the accuracy of classifier 1 are almost the same as in classifier 2. After 30 epochs of training, the classification accuracy of MCD achieves over 90% and remains stable. In the second chart, the accuracies of classifiers in MCD with the pseudo label fluctuate around 90%. However, the accuracies of these two classifiers are different. It is easy to determine that classifier 1 achieves higher accuracy than classifier 2. This difference may be because they are trained in a mixed domain. At the same time, they are optimized for minimizing the domain losses for both domains. Furthermore, the pseudo-labeling process in MCD with the pseudo-label introduces additional noise and uncertainty into the training data, which can lead to different classifiers and different accuracies. Figure 8: Accuracy and loss plot of DANN. Figure 9 represents the discrepancy loss from MCD and MCD with the pseudo label. It is clearly shown that the discrepancy loss of MCD remains below 0.05. As training continues, its discrepancy loss approaches 0. In contrast, the discrepancy loss of MCD with the pseudo label fluctuates wildly between 0 and 0.3. As training continues, its discrepancy loss fluctuates slightly but is still unstable. The fact that the feature distributions in these two distinct domains are aligned using discrepancy loss, which is determined using the variances between the model's predictions on these data, might be one explanation for this. However, in MCD with the pseudo label, pseudo-labels are used to designate data not labeled in the objective domain, which introduces some noise into the training data. As a result, the discrepancy loss in MCD with the pseudo label can fluctuate more wildly than that of MCD as the model tries to adapt to the noisy and uncertain pseudo labels and align the feature distributions in these two domains. Consequently, the optimization process may become more challenging and unstable as the model seeks to reconcile the conflicting goals of matching the feature distributions with minimized losses in these two domains. The table below gives more details for comparing MCD with MCD using pseudo-label semi-supervised learning. This strategy allows test samples with pseudo labels to be added to the data set when training this model. The details are shown below. Table 3 gives information about the average accuracy, best accuracy, average discrepancy loss, and training time of MCD using different training strategies. The best testing accuracy for both strategies is 100%. The average accuracy of the model using pseudo labels is 94.14%, which is approximately 15% higher than the model without pseudo labels. MCD can improve average accuracy with pseudo-labeling because pseudo-learning allows the model to use unlabeled data, which can provide additional detail about the distribution of the underlying data that can be used to make more accurate predictions. In addition, pseudo-tags can help regularize models, reduce overfitting, and improve accuracy. In addition, the average discrepancy losses for these two strategies are 0.015 and 0.093, respectively, and the training time for both is 139 s.