发动机润滑油是保障汽车发动机持久且稳定运转的基石, 准确评定发动机润滑油各项性能指标是其在生产到使用全过程必不可少的步骤。 发动机润滑油在一段时间的使用后会因为多种原因引起油品变质, 发动机润滑油变质的指标可以用其中非磁性颗粒物浓度、 金属屑含量、 pH值、 粘稠度、 含水率等表述。 关于发动机润滑油含水量的检测, 传统的检测方法存在操作复杂, 及时性差等缺点。 太赫兹对水吸收强烈, 适合用于对样品中微水含量的分析。 通过透射式太赫兹时域光谱系统获得1.0~3.5 THz下的六种不同水含量的发动机润滑油的吸收系数谱线, 对谱线进行Savitzky-Golay(SG)平滑去噪, 剔除奇异样本后, 采用Kennard-Stone算法划分样品集, 尝试常规区间偏最小二乘法(iPLS)、 向后区间偏最小二乘法(BiPLS)和联合区间偏最小二乘法(SiPLS)对其太赫兹时域光谱特征谱区间进行筛选, 着重研究区间间隔数、 PLS组件数、 最佳主因子数和区间选择等因素对PLS模型属性的影响, 并且对不同含水量的润滑油建模分析, 对不同模型比较选优, 建立最优定量分析模型。 建模结果表示特征谱区筛选可以提高建模性能、 降低模型复杂性, 特征谱区筛选算法通过剔除发动机润滑油太赫兹吸收系数谱线中非线性或者无关变量的方式, 使建模结果更好的表达吸收系数谱线与其含水量的关系。 结果表明: 采用BiPLS模型用于发电机润滑油中微量水含量的定量分析时建模效果最佳, 模型区间数为26, 入选区间为[18 10 4 3 8 12 5 11 24 13 16 21 2], 主因子数为10, 最优模型的交互验证均方根误差RMSECV为0.003 5, 预测均方根误差RMSEP为0.004 6, 校正集相关系数 r为0.919 3, 预测集相关系数 r为0.865 7。 由此可见, 可以采用反向区间偏最小二乘法(BiPLS)用于发动机润滑油水含量的测定, 且实验过程简单, 建模计算速度快, 效果理想, 可以适用于非接触式油品含水量的定量分析。

Engine lubricating oil is the cornerstone to ensure the long-term and stable operation of automobile engines. Accurately evaluating various performance indicators of engine lubricating oil is an essential step in the entire process from production to use. Engine lubricating oil will deteriorate for a variety of reasons after being used for a while. The engine lubricating oil deterioration indicators can be expressed in terms of non-magnetic particulate matter concentration, metal filings content, pH value, viscosity, water content and so on. To detect water content in engine lubricating oil, the traditional detection methods have the disadvantages of complicated operation and poor timeliness. Terahertz has strong absorption of water and is suitable for analysing micro-water content in sample products. In this paper, the transmission coefficients of six engine oils with different water contents were used to obtain the absorption coefficient curve of 1.0~3.5 THz by the transmission terahertz time domain spectroscopy system. The spectroscopic data were preprocessed with Savitzky-Golay(SG).Then, the sample was divided into a calibration set and test set by the Kennard-Stone algorithm after rejecting the odd samples.The interval Partial Least Squares (iPLS), backward interval partial least squares (BiPLS), and synergy interval partial least squares (SiPLS) were used to screen their terahertz time-domain spectral characteristic spectral intervals. They were focusing on the impact of factors such as the number of intervals, the number of PLS components, the number of best principal factors, and the selection of intervals on the PLS model’s properties. It also models and analyzes lubricants with different water contents, compares and selects different models, and establishes an optimal quantitative analysis model. The modeling results indicate that the feature spectrum region filtering can improve modeling performance and reduce model complexity. The characteristic spectrum region screening algorithm eliminates the non-linear or irrelevant variables in the terahertz absorption coefficient spectrum of engine lubricants so that the modeling results can better express the relationship between the absorption coefficient spectrum and its water content. The results show that the optimal model for quantitative analysis of trace water content in generator lubricants was obtained with BiPLS method that separated the whole spectra into 26 intervals and selected [18 10 4 3 8 12 5 11 24 13 16 21 2] intervals. The number of major factors is 10. The BiPLS model had a root mean standard error of cross-validation (RMSECV)of 0.003 5 and root mean standard error of prediction(RMSEP) of 0.004 6. The correlation coefficient ( r) of the correction set is 0.913 9, and the correlation coefficient ( r) of the prediction set is 0.865 7. Overall, BiPLS method could accurately predict the water content of engine lubricants, and the experimental process is simple, the modeling and calculation speed is fast, and the effect is ideal, and it can be applied to the quantitative analysis of the water content of non-contact oil products.