|Table of Contents|

Application of NIRS analysis technology in liquid propellant quality detection(PDF)

《火箭推进》[ISSN:1672-9374/CN:CN 61-1436/V]

Issue:
2018年02期
Page:
82-87
Research Field:
测控与试验
Publishing date:

Info

Title:
Application of NIRS analysis technology in liquid propellant quality detection
Author(s):
WANG Juxiang QU Jun XING Zhina LIU Jie
Coast Defense Army College, Naval Aviation University, Yantai 264001, China
Keywords:
NIRS liquid propellant PLS iPLS BP-ANN
PACS:
V511-34
DOI:
-
Abstract:
Application of near-infrared spectrum(NIRS)spectroscopy in liquid propellant quality determination is introduced in this paper. The spectrum preprocessing, waveband selection and modeling method of NIR are analyzed. The influence of high frequency noise and base line drift on information extraction can be effectively eliminated by spectral pretreatment methods, such as smoothing, differential coefficient, orthogonal signal correction or wavelet transform. NIRS is absorption peak of frequency multiplication of hydrogenous radical. Modeling in right wavelength range which is selected according to radical contribution of component under determination can simplify the model and improve accuracy of the analytical result. Partial least square(PLS), a common multivariate calibration method, is often used to set up the accurate calibration model for most analysis items. The interval partial least squares(iPLS)algorithm and BP artificial neural network(ANN)algorithm can be adopted for some components with low content and not abundant information or the components which are seriously disturbed by other components to improve the accuracy and predictive ability of calibration model.

References:

[1] 陆婉珍. 现代近红外光谱分析技术[M].北京:中国石油出版, 2007.
[2] 刘福莉, 陈华才. 近红外透射光谱法检测三组分食用调和油含量的研究[J]. 光谱学与光谱分析, 2009, 29(8): 2099-2102.
[3] 褚小立, 袁洪福, 陆婉珍. 近红外分析中光谱预处理及波长选择方法进展与应用[J]. 化学进展, 2004, 16(4): 528-542.
[4] 王菊香, 邢志娜, 申刚,等.光谱预处理和波长选择对混胺燃料各指标近红外光谱定量分析结果的影响比较[J]. 计算机与应用化学, 2013, 30(1):39-42.
[5] 王菊香, 邢志娜, 刘洁, 等. 近红外波长选择结合偏最小二乘法测定混胺中微量水分[J]. 分析试验室,2011, 30(11): 52-55.
[6] 郑永梅,张铁强,张军,等. 平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究[J]. 光谱学与光谱分析. 2004,24(16):1546-1548.
[7] 李华, 王菊香, 邢志娜, 等. 基于正交信号校正的喷气燃料近红外光谱去噪研究[J]. 计算机与应用化学, 2011, 28(1):97-102.
[8] 田高友,褚小立,袁洪福. 小波变换-偏最小二乘法用于柴油近红外光谱分析[J]. 计算机与应用化学. 2006, 23(10): 971-974.
[9] 孙百红,宋少伟. 基于小波分析的发动机转动惯量测量信号特征提取[J]. 火箭推进. 2010, 36(6): 52-55.
SUN Baihong, SONG Shaowei. Feature extraction for measurement signal of engine rotary inertia moment based on wavelet analysis [J]. Journal of rocket propulsion, 2010, 36(6): 52-55.
[10] 李华, 王菊香. 光谱预处理方法对混胺近红外定量模型影响的研究[J]. 分析科学学报, 2010, 26(5): 551-554.
[11] 谷筱玉, 徐可欣, 汪曣. 波长选择算法在近红外光谱法中药有效成分测量中的应用[J].光谱学与光谱分析, 2006, 26(9): 1618.
[12] 王菊香, 邢志娜, 叶勇, 等. 近红外光谱法快速分析液体推进剂组成和性质[J]. 理化检验化学分册, 2009, 45(7): 787-790.
[13] 褚小立. 化学计量学方法与分析光谱分子技术[M].北京:化学工业出版社,2011.
[14] XING Zhina, WANG Juxiang, YE Yong, et al. Rapid quantification of kinematical viscosity in aviation kerosene by near-infrared spectroscopy [J]. Energy & fuels, 2006, 20: 2486-2488.
[15] 王菊香, 申刚, 邢志娜. 近红外光谱快速测定混胺组分含量[J]. 分析化学, 2004, 32(4): 459-463.
[16] 邢志娜,王菊香,申刚.CCD近红外光谱分析技术在测定红烟硝酸中的应用[J]. 分析科学学报,2004,20(3): 278-282.
[17] 邢志娜,王菊香,申刚,等.改进偏最小二乘法在航空煤油的近红外光谱分析中的应用[J]. 兵工学报,2010,31(8): 1106-1109.
[18] 丁伟程, 吴建军, 刘洪刚. 基于神经网络算法的液体火箭发动机实时故障检测方法研究[J]. 火箭推进. 2005, 31(5): 5-10.
DING Weicheng, WU Jianjun, LIU Honggang. Real- time fault detection method based on neural network algorithm for LRE [J]. Journal of rocket propulsion, 2005, 31(5): 5-10.
[19] 韩晓, 王菊香, 刘洁, 等. 遗传算法结合神经网络用于傅里叶变换红外光谱法测定航空润滑油中水分[J]. 理化检验化学分册, 2012, 48(4): 388-391.
[20] 韩晓, 王菊香, 刘洁. 基于BP-神经网络的航空煤油总酸值近红外光谱快速检测[J]. 分析科学学报, 2011, 27(6): 751-75.

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