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A Novel Double-Stage Partial Adversarial Network in Cross-Domain Fault Diagnostics

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Kejia Zhuang
Xinyu Yang
Jun Hu
Published in
Proceedings of the 18th East Asia-Pacific Conference on Structural Engineering and Construction, Volume 3
Publisher
Springer Nature Singapore

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Abstract

This chapter introduces a novel double-stage partial adversarial network (DS-PAN) designed to enhance cross-domain fault diagnostics, particularly in bearing fault identification. The method addresses the challenges of partial transfer learning, where the label spaces of the source and target domains differ, potentially leading to negative transfer effects. The DS-PAN employs a unique double-stage classification approach and adaptive learning techniques to bridge the distributional gaps between domains, ensuring more comprehensive domain adaptation. Experiments conducted on a bearing dataset demonstrate the effectiveness of the DS-PAN method, showcasing its superior performance compared to other transfer learning methodologies. The results highlight the method's ability to achieve high diagnostic accuracy, even when the source and target domains share a limited label space. This innovative approach offers a robust solution for improving fault diagnosis in practical engineering settings.
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previous chapter Hybrid Supervised Model Based on Sample Quality Assessment for Rotating Machinery Fault Diagnosis with Limited Labelled Data
next chapter Using Supervised Variational Autoencoders to Detect Aerodynamic Erosion in Wind Turbines
Title
A Novel Double-Stage Partial Adversarial Network in Cross-Domain Fault Diagnostics
Authors
Kejia Zhuang
Xinyu Yang
Jun Hu
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Proceedings of the 18th East Asia-Pacific Conference on Structural Engineering and Construction, Volume 3

Print ISBN: 978-981-9500-89-5

Electronic ISBN: 978-981-9500-90-1

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-95-0090-1_64
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