基于可见-近红外光谱技术的润滑油含水量无损检测方法研究

研究了基于可见-近红外光谱技术的发动机润滑油含水量快速检测方法。在获取光谱信息的基础上,提出了采用不同的光谱建模方法以提高检测精度和简化分析计算。分别采用主成分分析(PCA)和连续投影算法(SPA)方法进行模型输入变量的提取。SPA最终选择了476,483,544,925,933,938,952,970和974nm共9个波长为最优变量。基于SPA选择的变量,分别应用偏最小二乘回归(PLSR)和多元线性回归(MLR)建模。效果均优于全波段PLSR模型和PCA-PLSR模型。说明SPA选择的有效变量能够包含最重要的全波段光谱信息,同时可以去除无用的信息变量。为了进一步提高检测效果,采用LS-SVM分别基于SPA选择后的有效变量和全波段光谱进行建模。两个模型的预测确定系数(Rp2)均在0.9以上。SPA-LS-SVM的效果要优于全波段LS-SVM模型的效果。SPA-LS-SVM模型的Rp2达到了0.983,剩余预测偏差(RPD)值为6.963。表明可见-近红外光谱可以用于发动机润滑油含水量的检测。

Non-invasive measurement of water content in engine lubricant using visible and near infrared spectroscopy

Visible and near-infrared reflectance spectroscopy (Vis-NIRS) was applied to non-invasively measurement of water content in engine lubricant. Based on measured spectra, several spectral calibration algorithms were adopted to improve accuracy and simply calculation. Principal component analysis (PCA) and successive projections algorithm (SPA) were separately used to reduce variables of spectral model. Nine effective variables, 476, 483, 544, 925, 933, 938, 952, 970 and 974 nm, were selected by SPA, and were inputted into partial least square regression (PLSR) and multivariable linear regression (MLR) models. Both the two models obtained better results than full-spectra-PLSR model and PCA-PLSR model. It shows that SPA does not select uninformative but effective variables from full-spectrum. Least-square support vector machine (LS-SVM) was operated to improve Vis-NIRS's ability based on full-spectrum and SPA, separately. High coefficients of determination for prediction set (Rp(2)) up to 0.9 were obtained by both full-spectrum-LS-SVM and SPA-LS-SVM models. SPA-LS-SVM is better than full-spectrum-LS-SVM. The value of Rp(2) of SPA-LS-SVM is 0.983 and residual predictive deviation (RPD) is 6.963. It is concluded that Vis-NIRS can be used in the non-invasive measurement of water content in engine lubricant, and SPA is a feasible and efficient algorithm for the spectral variable selection.