农业机械学报

鲁植雄,王雨彤,王琳,赵一荣,王兴伟,周俊博.基于IGWPSO-SVM的HMCVT湿式离合器摩擦副温度预测[J].农业机械学报,2023,54(10):407-415.
LU Zhixiong,WANG Yutong,WANG Lin,ZHAO Yirong,WANG Xingwei,ZHOU Junbo.Prediction of HMCVT Wet Clutch Friction Pair Temperature Based on IGWPSO-SVM[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):407-415.

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基于IGWPSO-SVM的HMCVT湿式离合器摩擦副温度预测 [下载全文]

Prediction of HMCVT Wet Clutch Friction Pair Temperature Based on IGWPSO-SVM [Download Pdf][in English]

投稿时间：2023-03-31

DOI：10.6041/j.issn.1000-1298.2023.10.041

中文关键词: 重型拖拉机湿式离合器温度预测支持向量机改进灰狼粒子群算法

基金项目:国家重点研发计划项目(2016YFD0701103)和拖拉机动力系统国家重点实验室开放项目(SKT2022006)

作者	单位
鲁植雄	南京农业大学
王雨彤	南京农业大学
王琳	拖拉机动力系统国家重点实验室
赵一荣	拖拉机动力系统国家重点实验室
王兴伟	拖拉机动力系统国家重点实验室
周俊博	南京农业大学

中文摘要:针对传统机器学习模型预测重型拖拉机液压机械无级变速箱(Hydro mechanical continuously variable transmission，HMCVT)湿式离合器温度的局限性，提出了基于改进灰狼粒子群优化-支持向量机(Improved grey wolf particle swarm optimization-support vector machine，IGWPSO-SVM)的HMCVT湿式离合器摩擦副温度预测模型。首先，对湿式离合器摩擦副滑摩过程进行热分析，确定影响湿式离合器摩擦副温度的因素；然后，基于支持向量机(Support vector machine，SVM)搭建温度预测模型，并利用改进灰狼粒子群优化(Improved grey wolf particle swarm optimization，IGWPSO)算法对SVM的结构参数进行优化；最后，基于HMCVT湿式离合器试验台数据搭建离合器摩擦副温度预测模型的样本数据库，以湿式离合器摩擦副对偶钢片为对象，对IGWPSO-SVM模型进行试验验证。试验结果表明，IGWPSO-SVM模型预测摩擦副对偶钢片内、中、外径温度的平均绝对误差(Mean absolute error，MAE)、均方误差(Mean square error，MSE)、均方根误差(Root mean square error，RMSE)、平均绝对百分比误差(Mean absolute percentage error，MAPE)的均值分别为3.3557℃、24.3212℃2、4.5976℃、3.95%，最高温度预测误差分别为7.8700、5.4300、0.9900℃，3次试验的对偶钢片内、中、外径温度MAE、MSE、RMSE、MAPE均值的平均值分别为3.3522℃、24.7380℃2、4.9737℃、4.12%，3次试验的内、中、外径最高温度平均绝对误差(Maximum temperature mean absolute error，MTMAE)平均值为4.3733℃，相比于其他4种已有的模型为最低。研究结果可为重型拖拉机湿式离合器温度的高精度预测及整车的可靠性控制提供理论依据。

LU Zhixiong WANG Yutong WANG Lin ZHAO Yirong WANG Xingwei ZHOU Junbo

Nanjing Agricultural University;State Key Laboratory of Power System of Tractor

Key Words:heavy tractor wet clutch temperature prediction support vector machines improved grey wolf particle swarm optimization algorithm

Abstract:Aiming at the limitations of traditional machine learning models in predicting the temperature of heavy tractor hydro mechanical continuously variable transmission(HMCVT)wet clutch, an improved grey wolf particle swarm optimization-support vector machine(IGWPSO-SVM) HMCVT wet clutch friction pair temperature prediction model was proposed. Firstly, the thermal analysis of the sliding friction of the wet clutch friction pair was conducted to determine the factors that affected the temperature of the wet clutch friction pair. Then a temperature prediction model was built based on support vector machine(SVM), and the structural parameters of SVM were optimized by using improved grey wolf particle swarm optimization(IGWPSO)algorithm. Finally, based on the HMCVT wet clutch test rig data, a sample database of the clutch friction pair temperature prediction model was established, and the IGWPSO-SVM model was tested and validated using the dual steel plate of the wet clutch friction pair. The experimental results showed that the mean absolute error(MAE), mean square error(MSE), root mean square error(RMSE), and mean absolute percentage error(MAPE)predicted by the IGWPSO-SVM model for the inner diameter, pitch diameter, and outer diameter of the dual steel sheet of the friction pair were 3.3557℃, 24.3212℃2, 4.5976℃ and 3.95%, respectively, the maximum temperature prediction errors were 7.8700℃, 5.4300℃ and 0.9900℃, respectively. The average values of three tests for MAE, MSE, RMSE and MAPE were 3.3522℃, 24.7380℃2, 4.9737℃ and 4.12%, respectively. The maximum temperature mean absolute error(MTMAE) for inner diameter, pitch diameter, and outer diameter was 4.3733℃, which was the lowest compared with that of the other four existing models. The research results can provide a theoretical basis for high precision prediction of temperature of wet clutch of heavy-duty tractors and reliability of entire vehicle.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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