Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis

The application of transfer learning to effectively identify rolling bearing fault has been attracting much attention. Most of the current studies are based on single-source domain or multi-source domains constructed from different working conditions of the same machine. However, in practical scenarios, it is common to obtain multiple source domains from different machines, which brings new challenges to how to use these source do-mains to complete fault diagnosis. To solve the issue, a conditional distribution-guided adversarial transfer learning network with multi-source domains (CDGATLN) is developed for fault diagnosis of bearing installed on different machines. Firstly, the knowledge of multi-source domains from different machines is transferred to the single target domain by decreasing data distribution discrepancy between each source domain and target domain. Then, a conditional distribution-guided alignment strategy is introduced to decrease conditional dis-tribution discrepancy and calculate the importance per source domain based on the conditional distribution discrepancy, so as to promote the knowledge transfer of each source domain. Finally, a monotone importance specification mechanism is constructed to constrain each importance to ensure that the source domain with low importance will not be discarded, which enables the knowledge of each source domain to participate in the construction of the model. Extensive experimental results verify the effectiveness and superiority of CDGATLN.

Automated summary

The application of transfer learning to identify rolling bearing fault has attracted much attention. However, most existing studies focus on single-source domains or multi-source domains constructed from different working conditions of the same machine. This study proposes a conditional distribution-guided adversarial transfer learning network with multi-source domains (CDGATLN) for fault diagnosis of bearings installed on different machines. The network transfers knowledge from multiple source domains to a single target domain and aligns conditional distributions to promote knowledge transfer. Experimental results demonstrate the effectiveness and superiority of CDGATLN.