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1.大连理工大学 机械工程学院, 辽宁 大连 116024
2.郑州机械研究所有限公司, 河南 郑州 450001
李胜甲(1995— ),男,河南原阳人,硕士研究生;研究方向为齿轮传动系统数字化设计与分析;lixiaojia5110608@163.com。
马雅丽(1963— ),女,辽宁鞍山人,博士,教授,博士生导师;研究方向为机械设计理论与方法;myl@dlut.edu.cn。
纸质出版日期:2023-03-15,
收稿日期:2022-03-03,
修回日期:2022-03-28,
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李胜甲,马雅丽,赵永生等.基于数据挖掘技术的齿轮传动系统啮合接触特性分析[J].机械传动,2023,47(03):78-85.
Li Shengjia,Ma Yali,Zhao Yongsheng,et al.Analysis of Meshing Contact Characteristics of the Gear Transmission System Based on Data Mining Technology[J].Journal of Mechanical Transmission,2023,47(03):78-85.
李胜甲,马雅丽,赵永生等.基于数据挖掘技术的齿轮传动系统啮合接触特性分析[J].机械传动,2023,47(03):78-85. DOI: 10.16578/j.issn.1004.2539.2023.03.011.
Li Shengjia,Ma Yali,Zhao Yongsheng,et al.Analysis of Meshing Contact Characteristics of the Gear Transmission System Based on Data Mining Technology[J].Journal of Mechanical Transmission,2023,47(03):78-85. DOI: 10.16578/j.issn.1004.2539.2023.03.011.
针对在多工况、多不确定性参数所形成的大数据下齿轮传动系统啮合接触特性分析困难的问题,提出了一种基于数据挖掘技术的齿轮传动系统啮合接触特性分析方法。基于多维高斯分布原理与齿轮传动系统有限元模型,构建了系统啮合接触特性数据集;采用最大信息系数分析了各系统参数与啮合接触特性之间的相关性,为预测模型提供了候选特征子集;采用支持向量机和随机森林算法建立了系统啮合接触特性预测模型,实现了对系统啮合接触特性的高效预测。结果表明,基于支持向量机算法的预测模型的预测误差最小,平均绝对百分比误差为3.87%,远小于理论计算误差。其中,在最优特征子集下,基于支持向量机算法的预测模型的各项接触特性预测误差指标显著下降,其平均绝对百分比误差降至3.03%,比优化前的接触特性预测误差减小了21.71%,验证了所提方法的精确性与有效性。
Aiming at the difficulty of analyzing the meshing contact characteristics of gear transmission system under the big data formed by multiple working conditions and uncertain parameters
a meshing contact characteristics analysis method of gear transmission system based on data mining technology is proposed. Based on the principle of multi-dimensional Gaussian distribution and the finite element model of gear transmission system
the meshing contact characteristic data set of the system is constructed. The correlation between system parameters and meshing contact characteristics is analyzed using the maximum information coefficient
which provides a candidate feature subset for the prediction model. Then
the prediction model of meshing contact characteristics of the system is established using support vector machine and random forest algorithm
which realizes the efficient prediction of meshing contact characteristics of the system. The results show that the prediction error of the prediction model based on support vector machine is the smallest
and the average absolute percentage error is 3.87%
which is far less than the theoretical calculation error. Under the optimal feature subset
the prediction error index of contact characteristics of the prediction model based on support vector machine decreases significantly
and its average absolute percentage error reaches 3.03%
which is 21.71% lower than the prediction error of contact characteristics before optimization. These have verified the accuracy and effectiveness of the proposed method.
齿轮传动系统啮合接触特性最大信息系数数据挖掘预测模型
Gear transmission systemMeshing contact characteristicsMaximum information coefficientData miningPrediction model
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