主成分分析在某型柴油机光谱数据分析中的应用

通过改变气缸套和活塞间隙，设计了6种工作状态，获得了某型6缸柴油机在每种工作状态下不同负荷时的润滑油样本共计69个，采用超谱M型原子发射光谱仪分析21种元素浓度。应用主成分分析法，分别以分析元素和润滑油样本为研究对象，基于权系数和主成分得分，对光谱数据进行了元素分类和样本分类。发现应用主成分分析法分析油液光谱数据能比较理想地对元素进行分类，该分类的3种主成分反映了5种类型的元素组合即磨损元素Fe，Cr，Cu，Al和Pb，含量较高的添加剂元素Na，Zn，P，Ca和Mg，含量较低的添加剂元素Ba和B，润滑油主成分元素C和H和干扰元素Ni，Ti，Mo，V和Ag等。不仅如此，主成分分析法对样本的分类较好地区分了来自柴油机不同的气缸套—活塞设计间隙的油样。研究表明应用主成分分析法分析润滑油光谱分析数据可揭示不同元素的来源、监测工况和诊断磨损故障。

Application of PCA to Diesel Engine Oil Spectrometric Analysis

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 coe...