金属氧化物半导体气体传感器气体检测关键问题研究
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摘要
易燃气体检测对防止煤矿瓦斯泄漏,监测石油化工行业安全生产,保证飞船、潜艇等密闭环境的人身安全具有重大意义。金属氧化物半导体(MOS)气体传感器因其具有结构简单、价格低廉、响应速度快、使用寿命长以及对可燃性气体和有机挥发性气体具有较高的灵敏度等优点而得到广泛应用,目前已成为世界上产量最大、应用最广的传感器。但由于存在交叉敏感、传感器漂移、加热功耗过大等问题,在实际应用中很难获得准确稳定的分析结果,直接影响着气体检测的精度。本文针对金属氧化物半导体气体传感器在易燃气体检测应用中的几个关键问题,进行了深入细致的研究,主要完成的工作如下:
为解决传感器的非线性响应特性及对非目标气体交叉敏感,研究了基于支持向量机(SVM)的传感器选择性改善方法。将4个MOS气体传感器组成传感器阵列,利用支持向量多分类机(MC-SVM)进行混合气体识别,应用最小二乘支持向量回归(LS-SVR)进行浓度测量。以浓度测量根均方误差作为泛化性能指标,利用训练样本的k遍历交叉验证结果作为目标函数,提出了基于小生境粒子群优化算法(NPSO)的参数寻优方法,能找到LS-SVR模型的全局最优参数。与其他阵列信号处理和模式识别方法如人工神经网络方法相比,该方法提高了气体识别准确率和浓度测量精度,特别适合于小样本气体检测的问题。
为抑制传感器的输出特性漂移问题,提出了基于盲源分离理论(BSS)的混合气体识别及传感器漂移补偿方法。建立了气体传感器阵列稳态响应的盲分离模型,论证了混合气体分析的盲可辨识条件。将未知气体浓度作为源信号,传感器阵列响应作为混合信号,设计了气体浓度和传感器稳态响应时间序列的构建方法。利用基于负熵的快速定点独立成分分析(FastICA)算法对气体传感器阵列稳态响应进行处理,不仅能够识别混合气体,同时能够去除传感器阵列非线性漂移的影响,对盲源分离理论的发展及其在传感器信息处理中的应用具有重要意义。
为降低传感器的加热功耗,研究了单传感器温度调制工作方式下的传感器动态响应特征提取技术,结合MC-SVM和LS-SVR方法实现混合气体识别与浓度测量。与传感器阵列方法相比,动态检测方法只需用一个传感器就可实现混合气体的组分分析,大大降低了传感器的加热功耗。研究了基于距离判据准则的传感器动态响应特征评估方法,解决动态响应特征参数选择难的问题。为抑制实际工作情况下噪声对传感器动态响应的影响,提出了基于小波奇异熵(WSE)理论的动态响应特征提取方法,与虚拟阵列(VA)、快速傅里叶变换(FTT)和离散小波变换(DWT)特征提取方法相比,在强噪声干扰下仍具有较高的泛化精度。
设计并实现了基于DSP的混合气体检测试验验证系统硬件平台,利用该平台完成了气体传感器的标定,在DSP上验证了传感器选择性改善、功耗性降低、单传感器动态检测等方法的有效性,实现了甲烷和氢气二元混合气体成分的在线测量。评测了传感器工作于恒温方式以及温度调制工作方式下各种算法(包括支持向量多分类机分类、最小二乘支持向量回归、动态响应特征提取等方法)的有效性,验证了系统的各项功能。该研究内容为易燃易爆危险品检测及有毒有害气体成分分析仪的开发奠定了基础。
Combustible gas detection is of great importance in preventing gas leakage of coal mine, monitoring safety condition of petrochemical industry, guarantying personnel safety in airship, submarine and other closed environments. Since metal oxide semiconductor (MOS) gas sensor has the advantages of simple structure, low cost, quick response, longevity of service, and high sensitivity to combustible gases and volatile organic gases, it has been most produced and widely used in the world. However, it is a bit difficult to obtain stable analysis result in the application since its sensitivity to environmental humidity, poor selectivity, drift and high heating power, which directly lead to the negative influence to the precision of gas detection. This paper focuses on several key issues related to combustible gas detection and hazard localization of MOS, and makes an in-depth study. Major tasks of this paper are as follows:
To solve the nonlinear response of MOS gas sensor and the cross-sensitivity to the non-target gases, this paper studies the support vector machines (SVM) based sensor selectivity improvement method. The sensor array comprises four MOS gas sensors. The gas category is identified by multi-classifier SVM (MC-SVM) and gas concentration is measured via least square support vector regression (LS-SVR). Given the root mean square error of concentration measurement as the criterion of generalization performance and using the k-fold cross-validation result of training sample as the objective function, the paper proposes a niche particle swarm optimization (NPSO) based parameter optimization algorithm which can find the global optimal parameters of the built LS-SVR model. Compared with other array signal processing and pattern recognition methods such as artificial neural networks (ANNs), this method improves the accuracy of gas recognition and the precision of concentration measurement, and it is especially suited for gas detection within small samples.
To restrain the influence of sensor output drift, this paper proposes blind source separation (BSS) based mixed gases recognition and sensor drift inhibition method. The blind separation model of gas sensor array steady-state response is built and the blind identification condition of gas mixture analysis is demonstrated. Given the unknown gas concentration as the source signals and the sensor array response as the mixed signals, this paper devises the time sequences for both gas concentration and sensor steady-state response. The negative entropy based fast fixed point independent component analysis (FastICA) algorithm is used to process the sensor array steady-state response. Experimental results demonstrate that this ICA based method can not only recognize the mixed gases but also remove the non-linear drift of the gas sensor array. It shows important significance for the development of BSS theory and its application in sensor information processing.
To reduce the heating power of gas sensor, this paper researches the single sensor dynamic response feature extraction technology under temperature modulation. The gas mixture classification and concentration measurement can be achieved in combination with MC-SVM and LS-SVR respectively. Compared with the sensor array based approach,this method that uses only one sensor to achieve gas identification and concentration measurement greatly reduce the sensor’s heating power. Also, this paper uses distance based class divisibility criterion as feature evaluation criteria to solve the difficulty of feature parameter selection of dynamic response. To restrain the noisy influence on the sensor dynamic response in the actual working condition, this paper proposes the wavelet singular entropy (WSE) based dynamic response feature extraction method. Experimental results show that, compared with virtual array (VA), fast Fourier transform (FFT) and discrete wavelet transform (DWT), the WSE based feature extraction method has a higher generalization accuracy when a strong noise exists.
Finally, this paper designs and implements the hardware platform of the mixed gas detection test sytem using DSP. Sensor calibration of this system is accomplished on this platform, and selectivity improvement, heating power reduction as well as signle sensor dymanic detection methods are verified on DSP. The system successfully implements the binary gas mixture analysis for CH4 and H2 online. Also, the paper evaluates the validity and the real-time ability of various alforithms (e.g. SVM multi-calssifier, LS-SVR and dymanic response feature extraction, etc) on DSP when sensor works in the constant temperature mode and in the temperature modulation mode, and verifies the whole function of the test system. This technology lays the foundation in the fields of combustible and explosive material detection as well as poisonous and harmful gas component analysis instruments.
为解决传感器的非线性响应特性及对非目标气体交叉敏感,研究了基于支持向量机(SVM)的传感器选择性改善方法。将4个MOS气体传感器组成传感器阵列,利用支持向量多分类机(MC-SVM)进行混合气体识别,应用最小二乘支持向量回归(LS-SVR)进行浓度测量。以浓度测量根均方误差作为泛化性能指标,利用训练样本的k遍历交叉验证结果作为目标函数,提出了基于小生境粒子群优化算法(NPSO)的参数寻优方法,能找到LS-SVR模型的全局最优参数。与其他阵列信号处理和模式识别方法如人工神经网络方法相比,该方法提高了气体识别准确率和浓度测量精度,特别适合于小样本气体检测的问题。
为抑制传感器的输出特性漂移问题,提出了基于盲源分离理论(BSS)的混合气体识别及传感器漂移补偿方法。建立了气体传感器阵列稳态响应的盲分离模型,论证了混合气体分析的盲可辨识条件。将未知气体浓度作为源信号,传感器阵列响应作为混合信号,设计了气体浓度和传感器稳态响应时间序列的构建方法。利用基于负熵的快速定点独立成分分析(FastICA)算法对气体传感器阵列稳态响应进行处理,不仅能够识别混合气体,同时能够去除传感器阵列非线性漂移的影响,对盲源分离理论的发展及其在传感器信息处理中的应用具有重要意义。
为降低传感器的加热功耗,研究了单传感器温度调制工作方式下的传感器动态响应特征提取技术,结合MC-SVM和LS-SVR方法实现混合气体识别与浓度测量。与传感器阵列方法相比,动态检测方法只需用一个传感器就可实现混合气体的组分分析,大大降低了传感器的加热功耗。研究了基于距离判据准则的传感器动态响应特征评估方法,解决动态响应特征参数选择难的问题。为抑制实际工作情况下噪声对传感器动态响应的影响,提出了基于小波奇异熵(WSE)理论的动态响应特征提取方法,与虚拟阵列(VA)、快速傅里叶变换(FTT)和离散小波变换(DWT)特征提取方法相比,在强噪声干扰下仍具有较高的泛化精度。
设计并实现了基于DSP的混合气体检测试验验证系统硬件平台,利用该平台完成了气体传感器的标定,在DSP上验证了传感器选择性改善、功耗性降低、单传感器动态检测等方法的有效性,实现了甲烷和氢气二元混合气体成分的在线测量。评测了传感器工作于恒温方式以及温度调制工作方式下各种算法(包括支持向量多分类机分类、最小二乘支持向量回归、动态响应特征提取等方法)的有效性,验证了系统的各项功能。该研究内容为易燃易爆危险品检测及有毒有害气体成分分析仪的开发奠定了基础。
Combustible gas detection is of great importance in preventing gas leakage of coal mine, monitoring safety condition of petrochemical industry, guarantying personnel safety in airship, submarine and other closed environments. Since metal oxide semiconductor (MOS) gas sensor has the advantages of simple structure, low cost, quick response, longevity of service, and high sensitivity to combustible gases and volatile organic gases, it has been most produced and widely used in the world. However, it is a bit difficult to obtain stable analysis result in the application since its sensitivity to environmental humidity, poor selectivity, drift and high heating power, which directly lead to the negative influence to the precision of gas detection. This paper focuses on several key issues related to combustible gas detection and hazard localization of MOS, and makes an in-depth study. Major tasks of this paper are as follows:
To solve the nonlinear response of MOS gas sensor and the cross-sensitivity to the non-target gases, this paper studies the support vector machines (SVM) based sensor selectivity improvement method. The sensor array comprises four MOS gas sensors. The gas category is identified by multi-classifier SVM (MC-SVM) and gas concentration is measured via least square support vector regression (LS-SVR). Given the root mean square error of concentration measurement as the criterion of generalization performance and using the k-fold cross-validation result of training sample as the objective function, the paper proposes a niche particle swarm optimization (NPSO) based parameter optimization algorithm which can find the global optimal parameters of the built LS-SVR model. Compared with other array signal processing and pattern recognition methods such as artificial neural networks (ANNs), this method improves the accuracy of gas recognition and the precision of concentration measurement, and it is especially suited for gas detection within small samples.
To restrain the influence of sensor output drift, this paper proposes blind source separation (BSS) based mixed gases recognition and sensor drift inhibition method. The blind separation model of gas sensor array steady-state response is built and the blind identification condition of gas mixture analysis is demonstrated. Given the unknown gas concentration as the source signals and the sensor array response as the mixed signals, this paper devises the time sequences for both gas concentration and sensor steady-state response. The negative entropy based fast fixed point independent component analysis (FastICA) algorithm is used to process the sensor array steady-state response. Experimental results demonstrate that this ICA based method can not only recognize the mixed gases but also remove the non-linear drift of the gas sensor array. It shows important significance for the development of BSS theory and its application in sensor information processing.
To reduce the heating power of gas sensor, this paper researches the single sensor dynamic response feature extraction technology under temperature modulation. The gas mixture classification and concentration measurement can be achieved in combination with MC-SVM and LS-SVR respectively. Compared with the sensor array based approach,this method that uses only one sensor to achieve gas identification and concentration measurement greatly reduce the sensor’s heating power. Also, this paper uses distance based class divisibility criterion as feature evaluation criteria to solve the difficulty of feature parameter selection of dynamic response. To restrain the noisy influence on the sensor dynamic response in the actual working condition, this paper proposes the wavelet singular entropy (WSE) based dynamic response feature extraction method. Experimental results show that, compared with virtual array (VA), fast Fourier transform (FFT) and discrete wavelet transform (DWT), the WSE based feature extraction method has a higher generalization accuracy when a strong noise exists.
Finally, this paper designs and implements the hardware platform of the mixed gas detection test sytem using DSP. Sensor calibration of this system is accomplished on this platform, and selectivity improvement, heating power reduction as well as signle sensor dymanic detection methods are verified on DSP. The system successfully implements the binary gas mixture analysis for CH4 and H2 online. Also, the paper evaluates the validity and the real-time ability of various alforithms (e.g. SVM multi-calssifier, LS-SVR and dymanic response feature extraction, etc) on DSP when sensor works in the constant temperature mode and in the temperature modulation mode, and verifies the whole function of the test system. This technology lays the foundation in the fields of combustible and explosive material detection as well as poisonous and harmful gas component analysis instruments.
引文
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