## 4 Experiments
### Analysis of Experimental Data of Bearing Failure Simulation on Bearing Life Prediction Test Bench
#### 4.2.2 Experimental Comparisons  
The TCA, JDA, and DACNN methods are validated with the performance of the proposed method presented herein. TCA utilizes kernel functions to map the number of samples into a high-dimensional regenerative kernel Hibert space, which narrows down the differences in the distribution of the pre-existing data and preserves the domain-invariable features. In TCA, the radial basis function is chosen as the kernel function of TCA. JDA (joint distribution adaptation) is a traditional transfer learning method, which extracts domain-invariable features and predicts the target domain by maximizing the difference in the distribution of the categories of the source and target domains for the mapping of each category. In JDA, radial basis functions are selected. The DACNN is also a migration learning model that learns domain-invariant features using a CNN with adversarial mechanisms.  
As illustrated in Table 8, the diagnostic accuracies of the TCA method and the JDA method between the three load conditions were 76.1% and 75.4%, which indicates pure kernel space mapping and two domain categories. It was difficult for the spatial mapping between them to obtain valuable features under various loading conditions. Compared to the present method, the DACNN method failed to regard the significance and uniqueness of the features of the recognition process in the feature learning process, and its recognition accuracy was lower. This paper's method further extracted the deep features in the samples by adding the channel reconstruction unit to the feature extractor species. Accordingly, the accuracy of the test between condition B and condition C reached 100%, and the mean accuracy of the three conditions was 99.4%, which is clearly better than that of the other methods.  
Figure 8: Time-domain and frequency-domain images of four health states of Skf6007 bearings.  
In the results shown in Figure 9, A and B are the source and target domains, respectively. As depicted in Figure 9,b, the feature categories learned by the TCA and JDA methods did not really perform effective alignment of the distributional differences, and most of the samples labeled 3 (outer-ring faults) were incorrectly predicted, which did not allow for effective feature identification and sample classification. While the method shown in Figure 9c improved the overall effect, the adversarial training, in a certain sense, made the network effectively learn the domain-invariable features, which positively affected the fault classification module in the prediction of the test samples. However, this paper's model was more capable of feature learning compared to the DACNN, indicating that it can more obviously recognize the various health states of the target domain bearings. The results of the above analysis show that this model is superior to the other three models, and the combination of the adversarial mechanism and the channel reconstruction unit can significantly improve the fault diagnosis performance of the bearings under different loading conditions.