Unsupervised structure subdomain adaptation based the Contrastive Cluster Center for bearing fault diagnosis

Recently, Unsupervised Domain Adaptation (UDA) as one of the transfer learning can handle the different data distributions and has been utilized in mechanical fault diagnosis under various working conditions successfully. However, most of them have only regarded two distributions as a global domain adaptation and ignored the subdomain adaptation issue, i.e., there is a subdomain distribution discrepancy between the two same categories. Additionally, most marking pseudo label approaches do not consider the influences of noise in pseudo labels. To circumvent the aforementioned challenges, firstly, a dropout trick has been developed and explored to filter the noisy pseudo label for obtaining the higher confident pseudo labels. Furthermore, a novel subdomain alignment method named Contrastive Cluster Center (CCC) has been proposed for pushing away the different domain cluster centers and bringing closer the same domain cluster centers for bridging the subdomain gap. Finally, the findings of the comparative experiments have demonstrated that the proposed method could boost the average accuracy of 2.2% and 3% on PU and LZUT bearing datasets than the state-of-art methods, respectively. Moreover, convergence analysis also suggests that the proposed method has superior robustness.