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| Study on Infrared Spectral Detection of Fuel Contamination in Mobil Jet Oil II Lubricating Oil |
| WANG Yan-ru, TANG Hai-jun*, ZHANG Yao |
Aviation Safety Technology Institute, China Academy of Civil Aviation Science & Technology, Beijing 100028, China
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Abstract Given a series of problems such as unplanned shutdown and flight failure caused by the fuel contamination of aviation engine lubricating oil, it is necessary to monitor the lubricating oil in use to determine the right time to change all lubricating oil. In this paper, Spectrum Two infrared spectrometer and Spectrum Quant software of PerkinElmer Company in the United States were combined with the American Society for Materials and Testing Standard (ASTM-E2412-10) on the monitoring instructions of synthetic ester lubricants. The fuel contamination degree of Mobil Jet Oil Ⅱ lubricating oil was quantitatively analyzed. The standard working curve of fuel contamination concentration and peak area of 815～805 cm-1 spectral region was established by using the two-point baseline area method. The correlation of the working curve was 0.999 6, and the standard prediction error was 0.544 1. The standard deviations of the five groups of repeated tests were all lower than 0.12%, which indicated that this method had high prediction accuracy and good repeatability. At the same time, the same lubricating oil samples were detected using the working curve and the fuel sniffer of Spectro Scientific company, and the results were similar, which indicated that the established quantitative working curve could meet the monitoring requirements of fuel contaminationin lubricating oil in civil aviation.
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Received: 2021-04-06
Accepted: 2021-07-12
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Corresponding Authors:
TANG Hai-jun
E-mail: tang_113@163.com
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[1] ZHANG Liang（张 椋）. New Technology&New Products of China(中国新技术新产品), 2019，(3): 31.
[2] Liu C, Tang X, Yu T, et al. Optik, 2020, 224: 165694.
[3] Abdul-Munaim A M, Aller M M, Preu S, et al. Tribology International, 2018, 119: 123.
[4] Pinheiro C T, Rendall R, Quina M J, et al. Energy & Fuels, 2017, 31(1): 179.
[5] Máquina A D V, Sitoe B V, Buiatte J E, et al. Fuel, 2019, 237: 373.
[6] Gong X, Tian H, Sun Y, et al. Research on Fuel-Dilution Monitoring of Engine Lubricant by FTIR Spectroscopy, 2016 5th International Conference on Energy and Environmental Protection (ICEEP 2016)，2016. 474.
[7] Wolak A, Krasodomski W, Zajc G. Friction, 2020, 8: 995.
[8] Zzeyani S, Mikou M, Naja J, et al. Tribology International, 2017, 114: 27.
[9] Yu Zhulin, Liu Jie, Zhao Bing. Study on Spectral Preprocessing and Wavelength Selection for Determing Kinematic Viscosity of Aviation Lubricating Oil by Infrared Spectroscopy, 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC). 2018. 1288.
[10] Saleh S H, Tripp C P. Talanta, 2021, 225: 121911.
[11] WANG Ju-xiang, WANG Kai（王菊香，王 凯）. Chinese Journal of Analysis Laboratory（分析试验室）, 2018, 37(7): 821.
[12] WANG Ju-xiang, LI Wei, QU Jun（王菊香，李 伟，瞿 军）. Analytical Instrumentation（分析仪器）, 2017，(2): 58.
[13] SUN Yun-ling, HE Wei, TIAN Hong-xiang, et al（孙云岭，何 伟，田洪祥，等）. Journal of Naval University of Engineering（海军工程大学学报）, 2018，(3): 82.
[14] YUE Cong-wei, ZONG Ying, JIANG Xu-feng, et al（岳聪伟，宗 营，姜旭峰，等）. Chemical Industry Times（化工时刊）, 2015, 29(5): 14.
[15] LIU Yu-jia, LANG Hong, HE Shan, et al（刘宇佳，郎 宏，何 山，等）. Aeroengine（航空发动机）, 2015, 41(4): 69.
[16] ASTM International. ASTM E2412-10—Standard Practice for Condition Monitoring of Used Lubricants by Trend Analysis Using Fourier Transform Infrared (FT-IR) Spectrometry, 2018.
[17] LI Chun-xiu（李春秀）. Synthetic Lubricants（合成润滑材料）, 2015, 42(4): 36.
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