热像仪探测泄漏气体的信噪比建模与测试

建立了能够定量预测热像仪探测泄漏气体能力的信噪比(SNR)与气体浓度(C)模型SNR-C, 利用该模型对制冷热像仪GasFindIRTM在SNR=1时对应的甲烷气体浓度进行预测, 预测数据与实际测试数据吻合。搭建了SNR-C室内测试装置, 测试了非制冷热像仪Photon320在以298、303、308、313和318 K面源黑体为背景时探测乙烯气体的SNR-C曲线。数据分析发现, Photon 320在SNR≈5时, 实测与预测乙烯浓度在各黑体背景温度下均比较接近。在SNR=5时, 模型预测的乙烯气体浓度分别为3146、987、570、394和298 ppm, 该变化规律与实际测量结果一致。建立的SNR-C模型能预测热像仪探测气体的能力, 而搭建的测试装置能定量测量热像仪探测泄漏气体时信噪比与气体浓度之间的变化关系, 可用于热像仪探测泄漏气体的室内性能测试。

Abstract

A SNR-C model to quantitatively predict the leak gas detection capability of thermal imager was proposed. Using this model, methane gas concentration of cooled thermal imager GasFindIRTM at SNR=1 was predicted, which was consistent with the measured results. The indoor testing setup was designed and built, SNR-C curves of uncooled thermal imager Photon320 to detect ethylene were tested at different temperatures of blackbody, namely 298, 303, 308, 313 and 318 K. Through analysis, measurement and prediction concentration at SNR≈5 were relatively close in all of five blackbody temperatures, and respectively prediction concentration are 3146, 987, 570, 394 and 298 ppm. And this variation trend was consistent with the measured concentration. Therefore, the SNR-C model put forward in this article was able to predict the gas detection capability of thermal imager. While the testing setup can quantitatively measure the relationship between SNR and gas concentration, which can be applied to test the indoor performance of gas leak detection of thermal imager.

参考文献

[1] Sandsten J, Edner H, Svanberg S. Gas visualization of industrial hydrocarbon emissions[J]. Optics Express, 2004, 12(7): 1443-1451.

[2] Kasai N, Tsuchiya C, Fukuda T, et al. Propane gas leak detection by infrared absorption using carbon infrared emitter and infrared camera[J]. NDT & E International, 2011, 44(1): 57-60.

[3] Lagueux P, Vallières A, Villemaire A, et al. Chemical agent standoff detection and identification with a hyperspectral imaging infrared sensor[C]//SPIE Europe Security+ Defence. International Society for Optics and Photonics, 2009, 7486: 74860C.

[4] Benson R, Madding R, Lucier R, et al. Standoff passive optical leak detection of volatile organic compounds using a cooled InSb based infrared imager[C]//AWMA 99th Annual Meeting, 2006: 131.

[5] Gross W, Hierl T, Scheuerpflug H, et al. Localization of methane distributions by spectrally tuned infrared imaging[C]//Photonics East (ISAM, VVDC, IEMB). International Society for Optics and Photonics, 1999: 234-240.

[6] Malm H, Gamfeldt A, von Würtemberg R M, et al. High image quality type-II superlattice detector for 3.3 μm detection of volatile organic compounds[J]. Infrared Physics & Technology, 2015, 70: 34-39.

[7] Niklaus F, Vieider C, Jakobsen H. MEMS-based uncooled infrared bolometer arrays: a review[C]//Photonics Asia 2007. International Society for Optics and Photonics, 2007, 6836: 68360D.

[8] Naranjo E, Baliga S, Bernascolle P. IR gas imaging in an industrial setting[C]//SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2010, 7661: 76610K.

[9] 李家琨, 顿雄, 金明磊, 等. 宽波段气体泄漏红外成像检测系统设计[J]. 红外与激光工程, 2014, 43(6): 1966-1971.

Li Jiakun, Dun Xiong, Jing Minglei. et al. Design of wide-band gas leak infrared imaging detection system[J]. Infrared and Laser Engineering, 2014, 43(6): 1966-1971. (in Chinese)

[10] 方辉, 雷述宇. 一种被动式气体成像系统: 中国, CN 201110247808.6[P]. 2011-8-24.

Fang Hui, Lei Shuyu. A passive gas imaging system: China, 201110247808.6[P]. 2011-8-24. (in Chinese)

[11] 王岭雪, 薛唯, 蔡毅, 等. 阿基米德螺旋线推扫滤光差分气体泄漏红外成像方法: 中国, 201310309389.3[P]. 2013-10-16.

Wang Lingxue, Xue Wei, Cai Yi, et al. A method of differential gas leak infrared imaging filtered by archimedes spiral filter: China, 201310309389.3[P]. 2013-10-16. (in Chinese)

[12] Long Y, Wang L, Li J, et al. Detectivity of gas leakage based on electromagnetic radiation transfer[C]//SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2011, 8013: 80130D.

[13] Benson R, Panek J A, Drayton P. Direct measurement of minimum detectable vapor concentrations using infrared optical imaging systems[C]//Proceeding of Air and Waste Management Association(AWMA) 99th Annual Conference and Exhibition, 2006.

[14] Li J, Jin W, Wang X, et al. MRGC performance evaluation model of gas leak infrared imaging detection system[J]. Optics Express, 2014, 22(107): A1701-A1712.

[15] Rodríguez-Conejo M A, Meléndez J. Hyperspectral quantitative imaging of gas sources in the mid-infrared[J]. Applied Optics, 2015, 54(2): 141-149.

[16] Golowich S E, Manolakis D G. Performance limits of LWIR gaseous plume quantification[C]//SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2011, 8048: 80481F.

[17] Holst G C. Testing and Evaluation of Infrared Imaging Systems[M]. US: SPIE, 2008: 63-68.

[18] Vollmer M, Mllmann K P. Infrared Thermal Imaging: Fundamentals, Research and Applications[M]. NY: John Wiley & Sons, 2010.

[19] 白廷柱, 金伟其. 光电成像原理与技术[M]. 北京: 北京理工大学出版社, 2006.

Bai Tingzhu, Jin Weiqi. The Principle and Technology of photoelectric Imaging[M]. Beijing: Beijing Institute of Technology Press. 2006. (in Chinese)

[20] 张成, 李春明. 红外热成像系统噪声等效温差测试研究[J]. 大理学院学报, 2009, 8(4): 37-39.

Zhang Cheng, Li Chunming. Research on NETD test of infrared imaging systems[J]. Journal of Dali University, 2009, 8(4): 37-39. (in Chinese)

罗秀丽, 唐璟, 王岭雪, 蔡毅, 薛唯, 张小水, 王书潜. 热像仪探测泄漏气体的信噪比建模与测试[J]. 红外与激光工程, 2016, 45(12): 1204003. Luo Xiuli, Tang Jing, Wang Lingxue, Cai Yi, Xue Wei, Zhang Xiaoshui, Wang Shuqian. Modeling and test of signal to noise ratio of leaking gas thermal imager[J]. Infrared and Laser Engineering, 2016, 45(12): 1204003.

热像仪探测泄漏气体的信噪比建模与测试

关于本站 Cookie 的使用提示

全站搜索

热像仪探测泄漏气体的信噪比建模与测试

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索