Co-training partial domain adaptation networks for industrial Fault Diagnosis

Gecheng Chen

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arXiv · 2410.16737

Co-training partial domain adaptation networks for industrial Fault Diagnosis

Gecheng Chen

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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.

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Gecheng Chen. 2024-10-22. Co-training partial domain adaptation networks for industrial Fault Diagnosis. https://arxiv.org/abs/2410.16737

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