热像仪探测泄漏气体的信噪比建模与测试

Modeling and test of signal to noise ratio of leaking gas thermal imager

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。

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 can quantitatively measure the relationship between test the indoor performance of gas leak detection of capability of thermal imager. While the testing setup SNR and gas concentration, which can be applied to thermal imager.