湿式离合器出油口甩出油温度预测方法
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摘要
为了实现对湿式离合器出油口甩出油温度传感器的冗余校验和自我诊断,提出了一种基于粒计算约简的离合器出油口甩出油温度的模糊预测方法。首先分析出油口甩出油温度的影响因素,将主要影响因素作为预测输入量,并采用模糊推理理论预测当前离合器出油口甩出油温度。在设计模糊预测方法的过程中,通过分析实车数据得到车辆行驶时离合器处于高滑摩功率过程和低滑摩功率过程的不同特性,分别确定相对应的隶属度函数和模糊预测规则,从而进一步提高出油口甩出油温的预测精度。为了提高模糊预测算法的实时性,基于模糊预测规则创建模糊决策表,模糊输入量和模糊输出量分别作为决策表的条件属性集与决策属性集。利用粒计算理论对模糊决策表的条件属性集进行属性约简,通过削减冗余信息有效降低模糊输入量和模糊预测规则的个数。最后利用实车采集的数据对比分析约简前后模糊预测算法的单步运行时间和预测误差等性能指标。试验结果表明:基于粒计算约简的模糊预测算法能够有效保障预测精度,同时拥有更少的模糊预测规则数和模糊输入量,有效解决了模糊预测算法占用资源较多以及实用性较差的问题。
In order to realize the redundancy check and self diagnosis of the oil temperature sensor at the wet dual clutch oil outlet, a fuzzy prediction method based on granular computing reduction was proposed. First, the factors that influence oil temperature at the wet dual clutch oil outlet were analyzed. These factors were then used as input for fuzzy reasoning to predict oil temperature. Different clutch characteristics under high and low friction power were obtained through analyzing actual vehicle data. This was utilized to determine the membership function and fuzzy prediction rule to enhance prediction accuracy. To improve real-time performance of the algorithm, fuzzy rules were applied to create a decision table, using fuzzy input and output quantities as conditional and decision attribute sets, respectively. Granular computing was employed to reduce the conditional attribute set of the fuzzy decision table, which effectively reduced the number of inputs and prediction rules. Finally, the fuzzy prediction algorithm's performance before and after reduction was analyzed using data collected from the vehicle. The results show that the fuzzy prediction algorithm that can effectively guarantee prediction accuracy based on granular computing reduction has fewer fuzzy prediction rules and inputs than the ordinary fuzzy prediction algorithm. This resolves the constraint of ordinary fuzzy prediction algorithm being too resource intensive for practical application.
In order to realize the redundancy check and self diagnosis of the oil temperature sensor at the wet dual clutch oil outlet, a fuzzy prediction method based on granular computing reduction was proposed. First, the factors that influence oil temperature at the wet dual clutch oil outlet were analyzed. These factors were then used as input for fuzzy reasoning to predict oil temperature. Different clutch characteristics under high and low friction power were obtained through analyzing actual vehicle data. This was utilized to determine the membership function and fuzzy prediction rule to enhance prediction accuracy. To improve real-time performance of the algorithm, fuzzy rules were applied to create a decision table, using fuzzy input and output quantities as conditional and decision attribute sets, respectively. Granular computing was employed to reduce the conditional attribute set of the fuzzy decision table, which effectively reduced the number of inputs and prediction rules. Finally, the fuzzy prediction algorithm's performance before and after reduction was analyzed using data collected from the vehicle. The results show that the fuzzy prediction algorithm that can effectively guarantee prediction accuracy based on granular computing reduction has fewer fuzzy prediction rules and inputs than the ordinary fuzzy prediction algorithm. This resolves the constraint of ordinary fuzzy prediction algorithm being too resource intensive for practical application.
引文
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[15] 陈泽华,谢刚,谢克明.粒矩阵与Rough关系矩阵[J].太原理工大学学报,2008,39(4):379-382.CHEN Ze-hua,XIE Gang,XIE Ke-ming.Granular Matrix and Rough Relation Matrix [J].Journal of Taiyuan University of Technology,2008,39 (4):379-382.
[16] 陈泽华,张裕,谢刚.基于粒计算的最简决策规则挖掘算法[J].控制与决策,2015,30(1):143-148.CHEN Ze-hua,ZHANG Yu,XIE Gang.Mining Algorithm for Concise Decision Rules Based on Granular Computing [J].Control and Decision,2015,30 (1):143-148.
[2] 赵彬,郑磊,汤一飞,等.干式离合器压盘表面温度线性计算方法研究[J].汽车实用技术,2017(4):166-168.ZHAO Bin,ZHENG Lei,TANG Yi-fei,et al.Study on Linear Calculation Method of Dry Clutch Surface Temperature [J].Automobile Applied Technology,2017 (4):166-168.
[3] 董玉才,杨万里,鞠红,等.基于偏最小二乘法的AMT离合器片表面温度预测[J].数学的实践与认识,2010,40(13):51-56.DONG Yu-cai,YANG Wan-li,JU Hong,et al.Prediction of Surface Temperature of AMT Clutch Plate Based of Partial Least Squares [J].Mathematics in Practice and Theory,2010,40 (13):51-56.
[4] 杨亚联,张喀,秦大同.湿式多片离合器热机耦合温度场及应力场分析[J].中国机械工程,2014,25(20):2740-2744.YANG Ya-lian,ZHANG Ka,QIN Da-tong.Research on Thermal Mechanical Coupling Temperature Field and Stress Field of Multiplate Wet Clutch Steel Disc [J].Chinese Journal of Mechanical Engineering,2014,25 (20):2740-2744.
[5] 熊玉杰,席军强,翟涌,等.干式离合器摩擦片表面温度测量与分析[J].广西大学学报:自然科学版,2016,41(2):451-459.XIONG Yu-jie,XI Jun-qiang,ZHAI Yong,et al.Research on Temperature Measurement of Dry Clutch Plate Based on Wireless Technology [J].Journal of Guangxi University:Nature Science Edition,2016,41 (2):451-459.
[6] LI W,HUANG J,FEI J,et al.Simulation and Application of Temperature Field of Carbon Fabric Wet Clutch During Engagement Based on Finite Element Analysis [J].International Communications in Heat & Mass Transfer,2016,71:180-187.
[7] 张红亮.湿式双离合器自动变速器液压控制系统分析与优化[D].长春:吉林大学,2014.ZHANG Hong-liang.Analysis and Optimization of Wet Dual Clutch Transmission's Hydraulic Control System [D].Changchun:Jilin University,2014.
[8] 潘强强,吴光强,罗少华,等.湿式双离合器带排扭矩研究[J].农业装备与车辆工程,2015,53(6):16-19.PAN Qiang-qiang,WU Guang-qiang,LUO Shao-hua,et al.Study on Drag Torque of Dual Wet Clutch [J].Agricultural Equipment & Vehicle Engineering,2015,53 (6):16-19.
[9] 项昌乐,章颖莹,刘辉.高转速差车用湿式离合器带排转矩特性研究[J].机械工程学报,2013,49(20):71-77.XIANG Chang-le,ZHANG Ying-ying,LIU Hui.Research on Drag Torque of High Relative Speed Open Wet Clutch of Vehicle [J].Journal of Mechanical Engineering,2013,49 (20):71-77.
[10] YUAN S H,PENG Z X,JING C B.Experimental Research and Mathematical Model of Drag Torque in Single-plate Wet Clutch [J].Chinese Journal of Mechanical Engineering,2011,24 (1):91-97.
[11] 贾方涛.多片湿式离合器冷却系统温度特性的仿真研究[D].长春:吉林大学,2014.JIA Fang-tao.Simulation Research of Temperature Characteristic on the Cooling System of Multi-disc Wet Clutch [D].Changchun:Jilin University,2014.
[12] 李士勇.工程模糊数学及应用[M].哈尔滨:哈尔滨工业大学出版社,2004.LI Shi-yong.Engineering Fuzzy Mathematics with Applications [M].Harbin:Harbin Institute of Technology Press,2004.
[13] CHIASELOTTI G,GENTILE T,INFUSINO F.Knowledge Pairing Systems in Granular Computing [J].Knowledge-based Systems,2017,124:144-163.
[14] 苗夺谦,王国胤,刘清,等.粒计算:过去、现在与展望[M].北京:科学出版社,2007.MIAO Duo-qian,WANG Guo-yin,LIU Qing,et al.Granular Computing:Past,Present and Outlook [M].Beijing:Science Press,2007.
[15] 陈泽华,谢刚,谢克明.粒矩阵与Rough关系矩阵[J].太原理工大学学报,2008,39(4):379-382.CHEN Ze-hua,XIE Gang,XIE Ke-ming.Granular Matrix and Rough Relation Matrix [J].Journal of Taiyuan University of Technology,2008,39 (4):379-382.
[16] 陈泽华,张裕,谢刚.基于粒计算的最简决策规则挖掘算法[J].控制与决策,2015,30(1):143-148.CHEN Ze-hua,ZHANG Yu,XIE Gang.Mining Algorithm for Concise Decision Rules Based on Granular Computing [J].Control and Decision,2015,30 (1):143-148.
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