基于润滑数值模型和状态参数的艉轴承性能衰变研究  

Performance decay of stern bearing based on lubrication numerical model and state parameters

作　　者：张涛[1] 郜慧敏 喻繁振 杨琨[1] ZHANG Tao;GAO Huimin;YU Fanzhen;YANG Kun(School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China;School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China)

机构地区：[1]武汉理工大学交通与物流工程学院,湖北武汉430063 [2]武汉理工大学船海与能源动力工程学院,湖北武汉430063

出　　处：《中国舰船研究》2022年第6期133-140,147,共9页Chinese Journal of Ship Research

基　　金：国家自然科学基金资助项目(52071241)。

摘　　要：[目的]为了实现对船舶艉轴承润滑状态的监测和评估,提出一种结合润滑性能衰变模型和支持向量机(SVM)算法的艉轴承润滑性能评估方法。[方法]针对船舶艉轴承润滑状态难以监测和识别的问题,建立轴承润滑衰变数值模型,并运用实验数据对该模型进行验证,研究载荷、粗糙度和半径间隙对润滑状态衰变机理的影响。基于SVM算法,构建润滑状态分类器,通过网格搜索算法优化超参数,利用不同润滑状态的数据集进行训练,最后实现对艉轴承润滑状态的评估。[结果]结果显示,随着外部载荷、粗糙度和半径间隙的增大,轴承润滑状态恶化的临界速度增大,动压润滑工作范围减小,混合润滑工作范围增大;由仿真数据集对润滑状态识别模型的验证表明,所提的润滑状态识别方法准确率达96.88%。[结论]所提方法能监测轴承的润滑性能特征,有效识别轴承的润滑状态。[Objective]This paper puts forward a method for monitoring and evaluating the lubrication performance of a marine stern tube bearing which combines a lubrication performance decay model and support vector machine(SVM)algorithm.[Methods]Aiming at difficulties in the monitoring and recognition of the lubrication regimes of stern bearings,a bearing lubrication decay numerical model is established and validated with experimental data.The effects of load,roughness and radius clearance on the lubrication decay mechanism are then investigated.Based on the SVM algorithm,a lubrication regime classifier is constructed;the hyperparameters are optimized through a grid search algorithm;the datasets of different lubrication regimes are used for training;and lubrication regimes for stern bearings are evaluated.[Results]The results show that with the increase of external load,roughness and radius clearance,the critical speed of the deterioration of the bearing lubrication regime increases,the working range of hydrodynamic lubrication(HL)decreases and the working range of mixed lubrication(ML)increases.The lubrication regime recognition model is then verified by the simulation dataset,and the proposed lubrication regime recognition method has an accuracy rate of96.88%.[Conclusion]The method proposed herein can monitor the lubrication performance characteristics of marine stern tube bearings and effectively identify the optimal bearing lubrication regime.

关 键 词：艉轴承 润滑性能 支持向量机 状态评估 

分 类 号：U664.21[交通运输工程—船舶及航道工程]