Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks.

Recently, transfer learning has been receiving growing interests in machinery fault diagnosis due to its strong generalization across different industrial scenarios. The existing methods generally assume identical label spaces, and propose minimizing marginal distribution discrepancy between source and target domains. However, this assumption usually does not hold in real industries, where testing data mostly contain a subspace of the source label space. Therefore, transferring diagnosis knowledge from a comprehensive source domain to a target domain with limited machine conditions is motivated. This challenging partial transfer learning problem is addressed in this study using deep learning-based domain adaptation method. A class weighted adversarial neural network is proposed to encourage positive transfer of the shared classes and ignore the source outliers. Experimental results on two rotating machinery datasets suggest the proposed method is promising for partial transfer learning.