As the important decomposition components of insulating gas sulfur hexafluoride, the accurate quantification of SO, CS and HS is important to determine the type and severity of internal faults in the insulating equipment. In this paper, a method combining symmetry dot pattern and deep learning is proposed for quantitative detection of multi-component gas mixtures on the ultraviolet absorption spectroscopy platform. The method is based on symmetry dot pattern analysis to fuse the local ultraviolet spectral information which is useful for identifying trace gas mixtures with overlapping absorption peaks. A deep network based on transfer learning is established to recognize the symmetry dot pattern diagrams, achieving feature representation from different scales. The outstanding performance of this method is demonstrated and compared to the existing models by receiver operating characteristic curve. Experimental results on the platform show that the proposed method can quantitatively detect the concentrations of SO, HS and CS ternary gas mixture online.

中文翻译：

---

具有对称点图案和深度学习的紫外吸收光谱法用于 SO2、H2S、CS2 混合气体的定量分析

作为绝缘气体六氟化硫的重要分解成分，SO、CS和HS的准确定量对于判断绝缘设备内部故障的类型和严重程度具有重要意义。本文提出了一种将对称点图案与深度学习相结合的方法，用于紫外吸收光谱平台上多组分气体混合物的定量检测。该方法基于对称点图案分析来融合局部紫外光谱信息，这对于识别具有重叠吸收峰的痕量气体混合物非常有用。建立基于迁移学习的深度网络来识别对称点图案图，实现不同尺度的特征表示。通过受试者工作特征曲线证明了该方法的优异性能，并与现有模型进行了比较。平台上的实验结果表明，该方法可以在线定量检测SO、HS和CS三元气体混合物的浓度。