金属氧化物CO传感器智能化方法研究
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摘要
传统金属氧化物CO传感器对H_2等还原性气体存在交叉响应，选择性差，且其性能易受环境温度和湿度影响，难以完全满足工程应用的需要。智能化是气体传感技术发展的重要方向，本文提出从检测方式和信号处理算法入手，提高传统CO传感器抗H_2干扰的能力，并尝试对环境温湿度进行补偿；通过大量实验，确定了有效的温度调制方法、机器学习算法以及环境温度补偿方案，研制了基于单片机的智能CO传感器模块。
本文设计了气体传感器测试系统，利用此系统先后对SnO_2型CO传感器MQ307A和TGS2442进行温度调制实验。考察了温度调制方式对MQ307A性能的影响；比较了快速傅立叶变换(FFT)和离散小波变换(DWT)两种特征提取方法，发现后者能够在特征空间对传感器在不同气体中的响应进行良好的分离；先后将支持向量机(SVM)和BP神经网络用于识别单一CO、单一H_2和CO／H_2混合物，并估计混合气体中CO的浓度。结果表明，支持向量机具有更高的正确识别率和估计精度；利用脉冲电压对TGS2442进行调制加热，从原始响应曲线和小波系数中提取分类特征，训练支持向量机模型，实现了对一定浓度范围的单一CO、单一H_2和CO／H_2混合气体的定性识别，并能够估计混合气体中CO的浓度。
研究了环境参数对MQ307A和TGS2442性能的影响。MQ307A受湿度影响很大，在高湿条件下工作后，其响应幅度逐渐变小；TGS2442采用了特殊的封装，它几乎不受湿度的影响，但是其基线电压随环境温度的变化而漂移。采用基于知识的温度补偿方案，利用TGS2442对20℃、40℃和60℃三种环境温度下的单一CO进行定量估计，结果表明温度补偿方法可行。
选用TGS2442作为基本传感器，研制了基于高性能单片机的智能CO传感器模块，利用DWT和SVM算法处理数据，使模块能够定性识别一定浓度范围的CO、H_2及其混合物，并估计混合物中CO的浓度，此外，模块还具有自检和温度自补偿等智能化功能，
Traditional metal oxide CO sensors are lack of selectivity for theircross-sensitivity to reductive gases, especially hydrogen gas. Besides, theirperformances are prone to be affected by ambient humidity and temperature.Increasing the intelligence level of gas sensor has been the mainstream in gas sensingtechnology. This paper aims to improve the H_2 resistibility of traditional CO sensor bymeans of dynamic working temperature control and self-learning algorithm, andcompensation for the effect of ambient humidity and temperature is also considered.After a large number of experiments, effective methods of temperature modulation,self-learning and temperature compensation are exploited, and a smart CO sensormodule is designed based on a high-performance rnicrocontroller.
A testing apparatus for gas sensor is developed and temperature modulationexperiments are conducted for the SnO_2-based CO sensor MQ307A and TGS2442 inturn. Various heating voltages for MQ307A are studied, and sinusoidal voltage isfound to be preferable. MQ307A's responses to CO, H_2 and CO/H_2 mixtures areacquired and Discret Wavelet Transform (DWT) is found to be better than FastFourier Transform (FFT) in extracting important features from the sensor's responsesignals. Support Vector Machine (SVM) and BP Artificial Neural Network (BP ANN)are then applied respectively to classify these three gases and furthermore, estimatethe concentrations of CO in CO/H_2 binary mixtures. The results demonstrate thatSVM gives higher success rate of classification and prediction accuracy than BP ANNdoes. TGS2442's responses to CO, H_2 and CO/H_2 mixtures are acquired and featuresextracted either from the original responses or wavelet coefficients are then processedby SVMs. Following this procedure, CO, H_2 and CO/H_2 mixtures are classifiedqualitatively, and the concentrations of CO in CO/H_2 mixtures are predicted.
Effects of ambient humidity and temperature on the performance of CO sensorare investigated. While MQ307A's output response is affected severely by relativehumidity, TGS2442 shows low humidity dependency due to its special encapsulation, however, the baseline of TGS2442 rises with the ambient temperature rising.Knowledge-based temperature compensation method has been adopted for TGS2442,and this method is validated in predicting the concentrations of CO at 20℃, 40℃and60℃respectively.
A microcontroller-based smart CO sensor module is designed using TGS2442 asthe sensing element, and the feature extraction and SVM algorithms are transportedinto the microcontroller. The results of validation experiments show that the smart COmodule is able to classify CO, H_2 and CO/H_2 mixtures qualitatively, and moreover,predict the concentration of CO in the CO/H_2 binary mixtures. Additionally, thismodule is characteristic of self-diagnose and ambient temperature compensation.
本文设计了气体传感器测试系统，利用此系统先后对SnO_2型CO传感器MQ307A和TGS2442进行温度调制实验。考察了温度调制方式对MQ307A性能的影响；比较了快速傅立叶变换(FFT)和离散小波变换(DWT)两种特征提取方法，发现后者能够在特征空间对传感器在不同气体中的响应进行良好的分离；先后将支持向量机(SVM)和BP神经网络用于识别单一CO、单一H_2和CO／H_2混合物，并估计混合气体中CO的浓度。结果表明，支持向量机具有更高的正确识别率和估计精度；利用脉冲电压对TGS2442进行调制加热，从原始响应曲线和小波系数中提取分类特征，训练支持向量机模型，实现了对一定浓度范围的单一CO、单一H_2和CO／H_2混合气体的定性识别，并能够估计混合气体中CO的浓度。
研究了环境参数对MQ307A和TGS2442性能的影响。MQ307A受湿度影响很大，在高湿条件下工作后，其响应幅度逐渐变小；TGS2442采用了特殊的封装，它几乎不受湿度的影响，但是其基线电压随环境温度的变化而漂移。采用基于知识的温度补偿方案，利用TGS2442对20℃、40℃和60℃三种环境温度下的单一CO进行定量估计，结果表明温度补偿方法可行。
选用TGS2442作为基本传感器，研制了基于高性能单片机的智能CO传感器模块，利用DWT和SVM算法处理数据，使模块能够定性识别一定浓度范围的CO、H_2及其混合物，并估计混合物中CO的浓度，此外，模块还具有自检和温度自补偿等智能化功能，
Traditional metal oxide CO sensors are lack of selectivity for theircross-sensitivity to reductive gases, especially hydrogen gas. Besides, theirperformances are prone to be affected by ambient humidity and temperature.Increasing the intelligence level of gas sensor has been the mainstream in gas sensingtechnology. This paper aims to improve the H_2 resistibility of traditional CO sensor bymeans of dynamic working temperature control and self-learning algorithm, andcompensation for the effect of ambient humidity and temperature is also considered.After a large number of experiments, effective methods of temperature modulation,self-learning and temperature compensation are exploited, and a smart CO sensormodule is designed based on a high-performance rnicrocontroller.
A testing apparatus for gas sensor is developed and temperature modulationexperiments are conducted for the SnO_2-based CO sensor MQ307A and TGS2442 inturn. Various heating voltages for MQ307A are studied, and sinusoidal voltage isfound to be preferable. MQ307A's responses to CO, H_2 and CO/H_2 mixtures areacquired and Discret Wavelet Transform (DWT) is found to be better than FastFourier Transform (FFT) in extracting important features from the sensor's responsesignals. Support Vector Machine (SVM) and BP Artificial Neural Network (BP ANN)are then applied respectively to classify these three gases and furthermore, estimatethe concentrations of CO in CO/H_2 binary mixtures. The results demonstrate thatSVM gives higher success rate of classification and prediction accuracy than BP ANNdoes. TGS2442's responses to CO, H_2 and CO/H_2 mixtures are acquired and featuresextracted either from the original responses or wavelet coefficients are then processedby SVMs. Following this procedure, CO, H_2 and CO/H_2 mixtures are classifiedqualitatively, and the concentrations of CO in CO/H_2 mixtures are predicted.
Effects of ambient humidity and temperature on the performance of CO sensorare investigated. While MQ307A's output response is affected severely by relativehumidity, TGS2442 shows low humidity dependency due to its special encapsulation, however, the baseline of TGS2442 rises with the ambient temperature rising.Knowledge-based temperature compensation method has been adopted for TGS2442,and this method is validated in predicting the concentrations of CO at 20℃, 40℃and60℃respectively.
A microcontroller-based smart CO sensor module is designed using TGS2442 asthe sensing element, and the feature extraction and SVM algorithms are transportedinto the microcontroller. The results of validation experiments show that the smart COmodule is able to classify CO, H_2 and CO/H_2 mixtures qualitatively, and moreover,predict the concentration of CO in the CO/H_2 binary mixtures. Additionally, thismodule is characteristic of self-diagnose and ambient temperature compensation.
引文
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