A multi-representation-based domain adaptation network for fault diagnosis

Deep learning-based domain adaptation algorithms with various representations have been recently developed to address the domain shift problem in mechanical fault diagnosis. However, few research have focused on potential improvements through multiple representations. Thus, a multi-representation-based domain adaptation network is proposed in this paper. Three complementary time-frequency representations are first proposed to serve as input-based multiple representations for the subsequent parallel models. Then, parallel models with improved inception modules are trained to obtain feature-based multiple domain-invariant representations. Finally, ensemble learning through majority voting is used to obtain the final results. Comprehensive experimental results on two test rigs reveal that the proposed algorithm outperforms state-of-the-art single-representation-based domain adaptation algorithms in terms of cross-domain fault diagnosis. Furthermore, visualization results demonstrate that the proposed algorithm extracts transferable features and takes advantage of ensemble learning to achieve high-precision diagnosis.

Automated summary

A domain adaptation network based on multiple representations is proposed, utilizing time-frequency representations and parallel models to obtain domain-invariant features, and achieving high-precision fault diagnosis through ensemble learning.