为实现对柴油机润滑油状态的在线评估，研究得到其介电常数、黏度随主要劣化指标（柴油含量、烟炱含量、氧化度）的变化规律：柴油含量对介电常数的影响较小，与黏度呈较明显负相关性；烟炱含量与介电常数呈正相关性，与黏度呈二次关系；氧化度对二者的影响很小，可以忽略。在此基础上，搭建了基于回归分析和BP神经网络的润滑油主要劣化指标评估模型。试验结果表明：BP神经网络模型对润滑油主要劣化指标具有更高的预测精度，对三者最大预测误差分别为其含量阈值的8.3%，9.7%，6%。采用所建模型对柴油机润滑油进行在线实时监测，可以避免因润滑油状态异常造成的发动机部件损坏，对提高柴油机工作的可靠性具有重要意义。

In order to realize the online evaluation of diesel engine lubricating oil condition, the variation laws of dielectric constant and viscosity with the main deterioration indicators such as diesel content, soot content and oxidation degree were acquired. The diesel content had little effect on the dielectric constant and showed an obvious negative correlation with viscosity. The soot content had a positive correlation with the dielectric constant and a quadratic relationship with the viscosity. The influence of oxidation degree on both could be ignored. Then an evaluation model of the main deterioration indicators for lubricating oil was established based on regression analysis and BP neural network. The experimental results show that BP neural network model has a higher prediction accuracy and the maximum prediction errors for main deterioration indicators are 8.3%, 9.7% and 6% of their respective content threshold. The online real-time monitoring of diesel engine lubricating oil by the built model can avoid damage to engine parts caused by abnormal state of lubricating oil and it is of great significance to improve the reliability of diesel engine.