Application of Partial Least-squares Regression to Oil Atomic Emitting Spectrum Data of a Type Diesel Engine
Aiming at relation between the concentrations of wearing elements of diesel engine and its loads(X1), cylinders clearances(X2, X3 and X4) and runtime after renewing oil(X5), the Partial Least-squares Regression(PLSR) has been used to analyze the oil atomic emitting spectrum data of a 6-cylinder diesel engine. The results show that Cu concentrations variance explained by the five components is largest These components are derived from X1, X2, X3, X4 and X5. The PLSR-function concerning Cu can forecast Cu concentrations well. It has proved perfect in forecasting all the Cu concentrations of the 69 samples in the seven kinds of operating conditions. The effect of X1, X2, X3, X4 and X5 upon Cu concentrations has been effectively evaluated by the Variable Important in Projection (VIP). As compared with the obvious effect of cylinders clearances(X2, X3 and X4) and that of runtime(X5), the effect of the loads is small(X1).
Diesel Engine Oil Atomic Emitting Spectrum Partial Least-squares Regression (PLSR) Variable Important in Projection (VIP) Wear
Yun-tao LIU Hong-xiang TIAN Yun-ling SUN Wen PEI
College of Naval Architecture and Power,Naval University of Engineering,Wuhan Hubei 430033,China
国际会议
厦门
英文
1657-1660
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）