红外与毫米波学报2018，Vol.37Issue(6)：704-710,7.DOI:10.11972/j.issn.1001-9014.2018.06.012
机动车尾气CO检测中神经网络多环境因子在线修正算法研究
Research on online correction algorithm with neural network multi-environment factors for CO detection of motor vehicle exhaust
摘要
Abstract
The influence of temperature,humidity and pressure on the measurement of exhaust gas CO concentration after pretreatment is analyzed.An on-line correction algorithm with multi-environment factors of neural network for the vehicle exhaust CO detection has been proposed.First,the exhaust gas sample data has been trained offline to build the BP neural network model,and then the real-time measured temperature,humidity,pressure and decimal absorption value of the samples have been put into the model for its online correction.Then the corrected CO concentration has been achieved,so the measurement error of the NDIR sensor caused by environmental changes has been solved.Through the prototype experiment,the simulation experiment and the comparison with SEMTECH-EcoStar,the maximum relative deviation of the CO with the concentration from 0 to 0.2％ is 4.8％when the temperature range is from 30 to 50℃,relative humidity is from 25 to 40％,the pressure is from 95 to 115 kPa.The experiments have been carried out in the vehicle field to get the correction factor between 0.8 and 1,which verifies the necessity and reliability of the method and provided effective technical support for the detection of the CO concentration of the high-temperature exhaust gas from motor vehicles.关键词
尾气CO检测/红外吸收/多环境因子/在线修正/BP神经网络Key words
CO exhaust detection/infrared absorption/multiple environmental factors/online correction/BP neural network分类
信息技术与安全科学引用本文复制引用
刘国华,张玉钧,张恺,唐七星,范博强,鲁一冰,尤坤,何莹,余冬琪..机动车尾气CO检测中神经网络多环境因子在线修正算法研究[J].红外与毫米波学报,2018,37(6):704-710,7.基金项目
Supported by the National Key Research and Development Program of China (2016YFC0201000),Anhui Science and Technology Major Project (15czz04124) （2016YFC0201000）
