|
|
|
|
|
|
| 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
|
|
|
|
|
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.
|
|
Received: 2021-04-06
Accepted: 2021-07-12
|
|
|
|
Corresponding Authors:
TANG Hai-jun
E-mail: tang_113@163.com
|
|
[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.
|
| [1] |
CHENG Jia-wei1, 2，LIU Xin-xing1, 2*，ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
| [2] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
| [3] |
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. |
| [4] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [5] |
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. |
| [6] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
| [7] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
| [8] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
| [9] |
LI Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
| [15] |
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. |
|
|
|
|
