氧化铟气体传感器动态测试及基于神经网络的识别特性研究  

Dynamic test and identification characteristics of indium oxide gas sensor based on neural network

作　　者：苑振宇 左凯源 李国成 张闯[1] 武越 YUAN Zhenyu;ZUO Kaiyuan;LI Guocheng;ZHANG Chuang;WU Yue(College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Beijing Institute of Spacecraft Environment Engineering,Beijing 100094,China)

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]北京卫星环境研究所,北京100094

出　　处：《航天器环境工程》2020年第6期628-635,共8页Spacecraft Environment Engineering

基　　金：国家自然科学基金项目(编号:61973058);辽宁省自然科学基金项目(编号:2020-KF-11-04);教育部中央高校基本科研业务费项目(编号:N2004028);北京卫星环境工程研究所创新基金项目(编号:CAST-BISEE 2019-007)。

摘　　要：准确测量密封舱内有毒有害气体成分和浓度是载人航天器环控生保系统的关键技术之一。文章选择In2O3作为气敏材料制备气体传感器,利用温度动态测试方法并结合神经网络模型对气体种类及浓度的检测进行研究,经静态和动态测试,获得了氧化铟气体传感器对不同种类、不同浓度气体的响应曲线。神经网络训练结果显示:训练模型可以识别6种不同的气体,识别率达98%以上;对不同气体不同浓度的检测误差小于10%,浓度梯度的平均识别率高于95%。The accurate measurement of the composition and the concentration of the toxic and harmful gas in a manned space capsule is one of the key technologies for the spacecraft environmental control and for the life support system.In this paper,In2O3 is selected as the gas sensing material to prepare the gas sensors,and the method of the dynamic temperature measurement combined with the neural network model is used to detect the type and the concentration of the gases.The responsive curves of the indium oxide gas sensor for gases of different kinds and different concentrations are obtained.It is shown that after the neural network,by the training model,six different kinds of gases can be identified,and the recognition rate is over 98%.The error for identifying different concentrations of gases is less than 10%,with an average rate of the gradient recognition of more than 95%.

关 键 词：气体传感器 IN2O3 BP神经网络 气体识别特性 温度动态测试 

分 类 号：TN710[电子电信—电路与系统]