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To mitigate the impact of sensor drift in hydrogen detection exposed to complex environmental interferences, this study proposes a Domain Adaptation Graph Neural Network (DAGNN). By leveraging Graph Attention Networks (GAT) to model nonlinear relationships among sensors and employing Representation Subspace Distance (RSD) for dynamic domain alignment, DAGNN effectively compensates for drift induced by environmental disturbances. Experiments were conducted using an array of five hydrogen sensors under 32 environmental conditions, yielding 960 samples. Results demonstrate that DAGNN achieves a MRE of 4.0% and a R2 of 0.99, outperforming existing methods. These findings underscore the potential of DAGNN in enhancing the robustness and precision of hydrogen sensing, offering valuable contributions to hydrogen safety monitoring technologies.

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Drift-Resistant Hydrogen Sensing via Domain Adaptation Graph Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Haifeng Se,
  • Kai Song,
  • Bo Wang,
  • Lu Xia

摘要

To mitigate the impact of sensor drift in hydrogen detection exposed to complex environmental interferences, this study proposes a Domain Adaptation Graph Neural Network (DAGNN). By leveraging Graph Attention Networks (GAT) to model nonlinear relationships among sensors and employing Representation Subspace Distance (RSD) for dynamic domain alignment, DAGNN effectively compensates for drift induced by environmental disturbances. Experiments were conducted using an array of five hydrogen sensors under 32 environmental conditions, yielding 960 samples. Results demonstrate that DAGNN achieves a MRE of 4.0% and a R2 of 0.99, outperforming existing methods. These findings underscore the potential of DAGNN in enhancing the robustness and precision of hydrogen sensing, offering valuable contributions to hydrogen safety monitoring technologies.