光谱学与光谱分析 |
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Application of PCA to Diesel Engine Oil Spectrometric Analysis |
LIU Tao, TIAN Hong-xiang*, GUO Wen-yong |
College of Naval Architecture and Power, Naval University of Engineering, Wuhan 430033, China |
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Abstract In order to study wear characteristics of a 6-cylinder diesel engine, six different working statuses were arranged by altering the clearance between cylinder and piston. Sixty-nine oil samples were taken from engine at different loads under 6 working statuses and analyzed by Spectroil M Instrument made in US. Principal component analysis (PCA) was applied to analyzing spectrometric data of sixty-nine oil samples and clustering those data according to elements and oil samples separately based on the weighted coefficient and principal component scores. All 21 elements were used in element clustering and only 6 wear-related elements, namely iron, chromium, aluminum, copper, plumbum and silicon, were used in sample clustering. It is shown that PCA effectively clustered oil spectrometric data into three different principal components according to elements. The projection of two different principal components exhibited five types of elements combinations, namely wear elements (Fe, Cr, Cu, Al and Pb), high concentration additives elements (Na,Zn,P,Ca and Mg), low concentration additives elements (Ba and B), base constituent of lubricating oils (C and H) and interferential elements (Ni,Ti,Mo,V,Ag and Sn). Furthermore, PCA clearly clustered oil samples according to different clearance between cylinder and piston in the diesel engine. The study suggests that analyzing oil spectrographic data by PCA could find the sources of different elements,monitor engine conditions and diagnose wear faults.
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Received: 2009-04-02
Accepted: 2009-07-06
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Corresponding Authors:
TIAN Hong-xiang
E-mail: hxtianwuhan@yahoo.com.cn
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[1] MAO Mei-juan, ZHU Zi-xin, WANG Feng(毛美娟,朱子新,王 峰). Technologies and Applications of Equipment Oil Monitoring(机械装备油液监控技术与应用). Beijing: National Defense Industry Press(北京:国防工业出版社),2006. [2] YAN Xin-ping, WANG Zhi-fang, YUAN Cheng-qing(严新平,王志芳,袁成清). China Plant Engineering(中国设备工程),2008:(8):8. [3] Peng Z, Kirk T B. Wear, 1999, 225-229: 1238. [4] LI Bing, ZHANG Pei-lin, CAO Zheng, et al(李 兵,张培林,曹 征,等). Lubrication and Engineering(润滑与密封), 2006,(4): 145. [5] Miloslav Pouzar, Tomas Cernohorsky, Anna Krejcova. Talanta, 2001, 54: 829. [6] Zhao C Y, Zhang H X, Zhang X Y, et al. Toxicology, 2006, 217: 105. [7] ZHANG Run-chu(张润楚). Multivariate Statistical Analysis(多元统计分析). Beijing: Science Press(北京:科学出版社),2006. 165. |
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