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October 20, 2025Eksploatacja i Niezawodnosc - Maintenance and ReliabilityOpen Access

A Physics-Guided Transfer Learning Framework with Consistency Verification for Cross-Domain Bearing Fault Diagnosis

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Authors

XJXinze JiaoJZJianjie ZhangJCJianhui Cao

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Overview

This framework enhances bearing fault diagnosis accuracy in diverse domains, indicating improved trustworthiness.

Key Points

  • PCTL significantly improves diagnostic accuracy while ensuring physical plausibility, and enhances model trust.
  • The method is based on a feedback loop that assesses diagnosis consistency with physical evidence.
  • Experiments show a strong correlation between predicted confidence and physical consistency in fault diagnosis.
  • This innovative approach supports the development of more interpretable and reliable diagnostic systems.

Cite This Study

Jiao et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcdc8d54a28a75cf227chttps://doi.org/10.17531/ein/211797
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  5. 5Research on Fault Diagnosis of Mechanical Bearings Based on Transfer Learning2025 · 3 citations