群智能搜索在基础油性能预测模型中的优化效能  

Optimization Efficiency of Swarm Intelligence Search in Base Oil Performance Prediction Model

作　　者：夏延秋 王裕兴 冯欣[1] 蔡美荣[2] XIA Yanqiu;WANG Yuxing;FENG Xin;CAI Meirong(School of Energy Power and Mechanical Engineering,North China Electric Power University,Beijing 102206,China 2.State Key Laboratory of Solid Lubrication,Lanzhou Institute of Chemical Physics,Chinese Academy of Sciences,Gansu Lanzhou 730000,China)

机构地区：[1]华北电力大学能源动力与机械工程学院,北京102206 [2]中国科学院兰州化学物理研究所固体润滑国家重点实验室,甘肃兰州730000

出　　处：《摩擦学学报》2023年第4期429-438,共10页Tribology

基　　金：中国国家重点研究与发展计划项目(2018YFB0703802);固体润滑国家重点实验室开放课题(LSL-1814)资助.

摘　　要：润滑油基础油组成成分的变化对其性能有重要影响,本文中针对复合基础油体系中KN4010、PAO40和PriEco 3000三种成分含量的不同配比,对润滑油运动黏度、黏度指数和旋转氧弹性能指标变化的影响,建立基于最小二乘支持向量机(LSSVM)基础性能预测模型,并进行对比分析及筛选;选用经典的粒子群优化算法(PSO)、蜻蜓算法(DA)和鲸鱼优化算法(WOA)等仿生群智能搜索策略构建混合模型,对优选的预测模型进行参数优化.测试结果表明:机器学习技术对油液性能具有良好的预测能力,并且LSSVM基础模型可以在小样本条件下得到相对较好的预测结果,而WOA-LSSVM能够显著降低模型的预测误差;并且通过测试和留一交叉验证法分析,WOALSSVM的预测结果明显优于其余模型,具有良好的预测精度和泛化能力.Changes in the composition of lubricant base oils have important effects on their performance.While traditional lubricant development needs to be carried out by repeatedly testing and profiling a large number of oil samples for performance,machine learning analysis and processing techniques for data can not only improve efficiency,save costs and eliminate human factor interference,but also make it possible to develop corresponding oil products in a precise and adjustable manner.This paper investigated the effects of different ratios of three component contents of mineral oil(KN4010),polyalpha-olefin synthetic oil(PAO40)and polyol ester(PriEco 3000)in the composite base oil system on the changes of lubricant kinematic viscosity,viscosity index and rotational oxygen bomb performance index.Firstly,a least squares support vector machine(LSSVM)based base performance prediction model was developed and compared with four algorithms,namely,random forest(RF),back propagation neural network(BPNN),extreme learning machine(ELM)and multiple linear regression(MLR),to examine the predictive ability of the base model on three physicochemical performance indicators,namely,kinematic viscosity,viscosity index and rotational oxygen bomb,for model selection.Then the classical Particle Swarm Optimization(PSO)algorithm and the emerging Dragonfly Algorithm(DA)and Whale Optimization Algorithm(WOA)in recent years are used to construct the hybrid prediction model,and the parameters of the preferred LSSVM base prediction model are optimized,i.e.,the kernel function width(σ2)and the regularization parameter(γ)are selected optimally.And examine the effects of different parameter optimization methods on the convergence speed,stability and prediction accuracy of the model under different physical and chemical properties with examples.Finally,the classical,base and hybrid models were evaluated and analyzed by comparing the prediction results of the models on the three performance metrics and the leave-one-out cross-validation(LOOCV)test,and th

关 键 词：基础油 含量配比 机器学习 性能预测 群智能优化搜索 

分 类 号：TH117.2[机械工程—机械设计及理论]