基于RBF神经网络的非色散红外SF_6气体传感器

Non-dispersive Infrared SF_6 Gas Sensor Based on RBF Neural Network

利用波段为2-20μm的电调制红外宽谱光源和中心波长为3.95μm及10.55μm的双通道热释电探测器,采用单光源双波长光路结构设计了一种新型SF6气体传感器.运用径向基函数神经网络对传感器在检测过程中因环境温度变化所带来的测量误差进行补偿,结果表明：SF6气体传感器在环境温度10-35℃、气体浓度0-0.200%范围内的检测准确度小于±1.5%FS,相对标准偏差为1.56%,可以有效消除在测量气体浓度时环境温度变化引起的非线性影响.与传统经验公式法和温度控制法相比,该方法具有良好的测量准确度和稳定性,且无需增加硬件温度补偿模块,有利于传感器的小型化和低成本设计.

The ro-vibrational spectra of the gas molecules is located in mid-infrared waveband, and then the information of the gas type and its concentration can be detected with a high precision based on the non-dispersive infrared technology. In this paper, a SF6 gas sensor was designed with the optical structure of the double light path of a single light, by utilizing a 2-20 μm electrically modulated thermal radiation source and a dual wavelength pyroelectric detector with the central wavelengths of 3.95 μm and 10.55 μm. The method of a radial basis function neural network algorithm was proposed to compensate the detection error caused by the variation of the ambient temperature. The experimental results show that the detection accuracy of this sensor is less than ±1.5%FS within the ambient temperature range of 10℃ to 35℃ and the gas concentrations from 0 to 0.200%. The relative standard deviation is 1.56%. It can effectively eliminate the nonlinear effects caused by the environmental temperature changing in measuring the gas concentration. Compared with the traditional compensation methods with the empirical formula or the temperature control scheme, our method has a better measuring accuracy and stability. Moreover, by using this method, the gas sensor doesn＇t need any temperature control module, which is beneficial to miniaturize the device size and reduce its cost.