## 4 Experimental Analysis and Verification
### Test III
Due to the simple structure and low computational complexity of DBN, IG-CWT-CNN, ICPW-HPF-CNN, and ML, these models require less time to complete fault diagnoses. S-Alexnet uses lightweight convolutional neural networks to extract features, which requires more time to complete a fault diagnosis. TF-MDA employs a one-dimensional convolutional neural network that integrates an attention mechanism to extract features, resulting in a longer time required for a fault diagnosis. JDA and DSAN require a large-scale convolutional neural network to extract raw signal features, resulting in significantly longer required time. Compared to TCA-DBN, the objective function of ITCA-DBN is more complex and has additional marking pseudo labels and SRPLC K-means steps, thus requiring more time. Combining Tables 6 and 7, compared with DBN and TCA-DBN, although ITCA-DBN takes longer than DBN and TCA-DBN, its accuracy is higher. Compared with S-Alexnet, DSAN, and JDA, ITCA-DBN not only requires less time but also has higher accuracy. Compared with the other different methods, the proposed method  
function to increase intraclass compactness. Additionally, to improve the accuracy of the pseudo labels in the validation set, SRPLC-K-means was designed to filter the noisy samples and correct the pseudo labels, resulting in higher confident pseudo labels. Based on two tests using the CWRU bearing dataset and a test using underground drum motor bearing data, the following conclusions are drawn:  
(1) ITCA can significantly reduce the distribution discrepancies of the same type of data between different domains, achieve subdomain adaptation, and improve the intraclass compactness of the samples. According to the results of the three tests, the proposed method achieves diagnostic accuracies of 99.445%, 98.906%, and 91.944%, respectively, surpassing other methods;  
(2) The proposed SRPLC-K-means contributed to diagnosing faults under variable working conditions;  
(3) Compared to end-to-end models, such as JDA and DSAN, the proposed method reduced the required time by 594.9 s and 1431.6 s, respectively, while increasing the accuracy by 22.291% and 5.555%, respectively. We should note that the proposed method is currently only applicable to datasets with balanced numbers of each fault type and from a single source domain. In the future, we plan to explore subdomain adaptations, clustering domain adaptation, and noise reduction algorithms to cater to more demanding industrial requirements. Conceptualization, D.L. ; methodology, D.L. ; software, D.L. ; validation, D.L. ; formal analysis, D.L. ; investigation, D.L. ; resources, D.L. ; data curation, D.L. ; writing--original draft preparation, D.L. ; writing--review and editing, M.M. ; visualization, D.L. ; supervision, M.M. ; project administration, M.M. ; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the Shanghai Sailing Program, grant number 22 YF1429500. The data used to support the findings of this study are available from the corresponding author upon request. The authors declare no conflicts of interest.