Abstract:The pollution concentration prediction model of lubricants based on the principle of reflection spectrum is studied. Pure oil and full pollution oil are chosen as experimental materials. Different concentration samples are gradually prepared by equivalent volume method. The spectrometer of 200~850 nm band range is used to measure the samples. Xenon lamp serves as excitation light source. After adjusting, remain unchanged the distance and geometry azimuth between the detector probe and the sample, spectral measurement can be just conducted. The spectral reflectance experimental results show the higher contamination of the lubricants, the lower intensity of the reflected spectrum. In the variable step size algorithm on the original experimental data sparse sampling based, Correlation Coefficient, PCA, and PCA combined with Correlation Coefficient method (PCA-Correlation Coefficient) are respectively adopted to select advantage characteristic wavelength of lubricating oil in the wide band of 220~780 nm, and the index regression model is established about oil pollution concentration and spectral reflectance. Test results show that when the concentration is greater than 0.06, the index regression model of pollution concentration and spectral reflectance at 378.93 nm which is selected by PCA-Correlation Coefficient can well realize the prediction and estimation of pollution concentration of lubricant oil. So the regression model based on PCA-Correlation Coefficient to select advantage wavelength is suitable for the quality estimation of lubricating oil with high pollution concentration. Because of the specific conditions of the concentration and mixed medium, the model does not obey Lambert-Beer Law, which apply to low concentration, uniform and transparent solution. The paper provides a feasible experimental basis for the further to achieve the working lubricants pollution concentration with reflection spectrum method online, fast and accurate determination.
Key words:Reflectance spectroscopy; Lubricants; Index model; PCA-Correlation Coefficient
[1] MU Lin, XIONG Li-ping(穆 琳, 熊丽萍). The Study of Lubricants Monitoring and Wear Mechanism Based on Atomic Spectroscopy. East China Jiaotong University(华东交通大学), 2015.
[2] HAN Xiao-ge, ZHANG Duo-duo(韩晓鸽, 张朵朵). China Petroleum and Chemical Standard and Quality(中国石油和化工标准与质量), 2010, 5: 278.
[3] TIAN Yong, LIAN Shu-lin, CHEN Min-jie(田 勇, 廉书林, 陈闽杰). Hydranlics Pneumatics & Seals(液压气动与密封), 2013,33(7):1.
[4] HOU Di-bo, ZHANG Jian(侯迪波, 张 坚). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(7): 1839.
[5] YE Zhou, LIU Li(叶 舟,刘 力). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(6): 1695.
[6] LIU Bing-xin, LI Ying(刘丙新,李 颖). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(4): 1100.
[7] Reuben N Okparanma, Abdul M Mouazen. Water Air Soil Pollut, 2013, 224: 1539.
[8] Lammonlia T, Filho C R D S. Remote Sensing of Environment, 2012, 115(10): 2525.
[9] TANG Jie-qiong, MA Qing-feng(唐洁琼, 马庆丰). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(3): 672.
[10] WANG Jin, YIN Bao-cai(王 瑾, 尹宝才). Research on Data Gathering, Restoration and Compression Based on Sparse Representation. Beijing: Beijing Industry University(北京:北京工业大学), 2015.
[11] He Y, Qin H. Proceedings of the Geometric Modeling and Processing, 2004. 279.
[12] SUN Wei, XU Jun-yi(孙 伟, 许君一). Journal of Computer-Aided Design and Computer Graphics(计算机辅助设计与图形学学报), 2004, 16(5): 619.