混合气体识别的响应等效性与瞬态信号正交分解模型

针对目前混合气体识别大多采用传感器稳态响应信号、基于线性混合假设或大量样本学习,而瞬态响应信号特征分析主要应用非正交分解的问题,提出一种基于响应等效性与瞬态信号正交分解的混合气体识别模型。分析金属氧化物半导体传感器对混合气体的响应特性,建立基于气体响应成分等效性假设的气体非线性混合模型,在此基础上,提出并应用一种新的正交基函数——扩展类Legendre正交基,对气体传感器瞬态响应信号进行分解;通过对正交分解系数与气体浓度的回归分析,验证二者之间的指数型关联关系,并以正交分解系数为特征参数,利用气体非线性等效混合模型对混合气体分解与辨识。实验结果表明,尽管这种混合气体识别模型仅用单一气体检测的先验知识,对混合气体的识别误差仍可达到15%以内。

Gas Mixture Estimation Model Based on Response Equivalent and Orthogonal Decomposition of Transient Response Signals

Most methods used to recognize and estimate gas mixtures are always adopt non-orthogonal decomposition of steady response signal, based on linear mixture assumption and/or training the classifiers by large amount of data. Considering less orthogonal decomposition approaches have been applied to analyze transient response signals, a gas mixture estimation model based on response equivalent and orthogonal decomposition of transient response signals was put forward in this study. A novel nonlinear gas-mixing model was built after the analysis of MOS gas sensor response characteristics based on the component equivalent expression for different gas responses. A new orthogonal basis function, the extended quasi-Legendre (EQL) basis, was proposed and applied to the decomposition of a transient response signal from the gas sensor. After the exponential relationship between the coefficients and the gas concentration was verified via regression analysis, the components in the gas mixture were separated, and their concentrations were estimated simultaneously. The experimental result shows that the concentration determined from the transient response of the gas mixture shows an error within the 15% range, even though only the prior knowledge of single gas responses was applied to build the model.