Co-training partial domain adaptation networks for industrial Fault Diagnosis

Gecheng Chen

Co-training partial domain adaptation networks for industrial Fault Diagnosis

Machine Learning 2024-11-05 v2

Authors: Gecheng Chen

Abstract

The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to mitigate the ADP problem. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN.

Keywords

domain adaptation fault diagnosis residual adaptation interactive learning industrial applications model selection

Cite

@article{arxiv.2410.16737,
  title  = {Co-training partial domain adaptation networks for industrial Fault Diagnosis},
  author = {Gecheng Chen},
  journal= {arXiv preprint arXiv:2410.16737},
  year   = {2024}
}

Co-training partial domain adaptation networks for industrial Fault Diagnosis

Abstract

Keywords

Cite

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