| 光谱学与光谱分析 |
|
|
|
|
|
| Research on High-Precision Temperature Measurement System Based on Near Infrared Spectroscopy |
| ZHANG Yu-cun, QI Yan-de*, FU Xian-bin |
| Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China |
|
|
|
|
Abstract At present, the interference from outside radiation under the complex environment is difficult to eliminate by using the infrared thermometry method. It leads to the low measurement accuracy. In the present paper, a high-precision infrared temperature measurement system was designed. The light filter method is presented in this system. The broadband filters and three-level interference filter were combined in this method. According to the method, the near-infrared spectra sent out by high temperature object is filtered. The high temperature background light and the environment obtrusive light are filtered out. In this way, two monochromatic spectra are obtained. The radiation power ratio is received after receiving by the infrared detector. Then the temperature is obtained by calculating. In this system, the bandwidth of monochromatic spectrum permeated is only 1 nm. The inhibition of radiation from background light and ambient light except transmittance spectrum is up to 8 orders of magnitude. The measurement error caused by the ambient temperature heating of the measured object is reduced. The accuracy of the temperature measurement system is improved. Finally, the temperature measurement system is feasible according to the experiment result. The precision reached to 0.2%.
|
|
Received: 2011-03-30
Accepted: 2011-07-10
|
|
|
|
Corresponding Authors:
QI Yan-de
E-mail: qiyandehao@126.com
|
|
[1] ZHENG Zhong, HE La-mei(郑 忠，何腊梅). Industrial Heating(工业加热), 2005, 34(3): 25.
[2] WANG Xi-bin, RONG Rui-fang(王喜斌，荣瑞芳). Laser & Infrared(激光与红外). 2004, 34(5): 3557.
[3] WANG Bo, ZHAO Guo-rong(王 波，赵果荣). Science & Technolog of Baotou Steel Corporationg(包钢科技), 2005, 31(1): 56.
[4] DAI Jing-min, YANG Mao-hua, CHU Zai-xiang(戴景民, 杨茂华, 褚载祥). Journal of Infrared Millimeter Waves(红外与毫米波学报), 1995, 14(6): 461.
[5] GE Hua-guang, WANG Sun-an, ZHAO Lin-hai(葛华光，王孙安，赵淋海). Modern Cast Iron(现代铸铁), 2006, (1): 92.
[6] SUN Zhi-yuan, LI Qing-an(孙志远，李清安). Chinese Journal of Scientific Instrument(仪器仪表学报), 2006, 27(6): 67.
[7] SHI De-heng, DAI Qi-run, CHEN Yu-ke(施德恒，戴启润，陈玉科). Chinese Journal of Scientific Instrument(仪器仪表学报), 2005, 26(12): 1238.
[8] ZHANG Jian, YANG Li, LIU Hui-kai(张 健，杨 立，刘慧开). Infrared Technology(红外技术), 2005, 27(5): 419.
[9] YANG Zhen, YANG Li(杨 桢，杨 立). Optical Technique(光学技术), 2008, Suppl.: 154.
[10] JIANG Shi-chang, CHEN Chu-xin(姜世昌，陈出新). J. Infrared Millim. Waves(红外与毫米波学报), 2001, 20(6): 426.
[11] FU An-ying, MA Rui(付安英，马 睿). Modern Electronics Technique(现代电子技术), 2007, 30(4): 4.
[12] CAI Lü-zhong, WANG Cheng-yan(蔡履中，王成彦). Optics(光学). Ji’nan：Shandong University Press(济南：山东大学出版社), 1992. 302.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|
