IF-EDAAN: An information fusion-enhanced domain adaptation attention network for unsupervised transfer fault diagnosis

Cuiying Lin, Yun Kong*, Qinkai Han, Ke Chen, Zhibo Geng, Tianyang Wang, Mingming Dong, Hui Liu, Fulei Chu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Unlike domain adaptation methods that rely on single-source information for transfer diagnosis, multi-source information-based domain adaptation methods can leverage the extensive diagnostic features derived from multiple sources of data. However, the issues of potential feature conflicts, the critical fault information loss, and high computational burdens still hinder effective applications of multi-source information domain adaptation for transfer diagnosis. For resolving these issues, this study proposes an unsupervised multi-source information domain adaptation approach for transfer fault diagnosis, which utilizes an information fusion-enhanced domain adaptation attention network (IF-EDAAN). Firstly, an information fusion method that converts multi-source information into a fused image using principal component analysis and signal-to-image conversion is employed to enhance and compress data from both the source and target domains. Then, a parameter-free attention mechanism (PFAM) module is proposed to adaptively focus on the domain-invariant temporal and spatial features of information fusion samples. Subsequently, the weight assignment module and joint maximum mean discrepancy metric strategy are proposed to mitigate negative transfer, thus enabling the effective extraction and alignment of domain-invariant temporal and spatial features. Finally, experiment validations on two rotating machinery datasets have been comprehensively elaborated to verify the efficacy and advantages of our proposed IF-EDAAN approach for transfer fault diagnosis across different working conditions. Experiment results have proved that our proposed IF-EDAAN approach can rapidly adapt to new transfer diagnostic scenarios with impressive performance and outperform several mainstream unsupervised domain adaptation approaches.

Original languageEnglish
Article number112180
JournalMechanical Systems and Signal Processing
Volume224
DOIs
Publication statusPublished - 1 Feb 2025

Keywords

  • Fault diagnosis
  • Multi-source information fusion
  • Transfer learning
  • Unsupervised domain adaptation

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Lin, C., Kong, Y., Han, Q., Chen, K., Geng, Z., Wang, T., Dong, M., Liu, H., & Chu, F. (2025). IF-EDAAN: An information fusion-enhanced domain adaptation attention network for unsupervised transfer fault diagnosis. Mechanical Systems and Signal Processing, 224, Article 112180. https://doi.org/10.1016/j.ymssp.2024.112180