1.湖南科技大学机电工程学院，湘潭;2.湖南星邦智能装备股份有限公司,长沙;3.复杂环境特种机器人控制技术与装备湖南省工程研究中心
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湖南省教育厅项目(23B0496)；国家博士后基金(2023M731822)；湖南省研究生科研创新项目（CX20240869）。
1.College of Electrical and Mechanical Engineering,Hunan University of Science and Technology,Xiangtan;2.Hunan Xingbang Intelligent Equipment Co,Ltd,Changsha;3.Hunan Engineering Research Center for Complex Environment Special Robot Control Technology and Equipment,Xiangtan
Project of Hunan Provincial Education Department (23B0496); National Postdoctoral Fund (2023M731822);Hunan Provincial Graduate Student Research Innovation Program(CX202408690).
针对不同工况下轴向柱塞泵效率理论计算公式和实验结果误差较大的问题，提出了基于机器学习的泵效率计算方法。首先建立了基于轴向柱塞泵效率的非线性回归模型，并通过显著性检验验证了回归模型的有效性；随后设计了一套基于BP神经网络的柱塞泵效率预测模型，并基于实验数据对预测模型进行了训练与验证；最后分别评估了两个模型的预测精度。结果表明：在变压力、变转速及变流量工况下，相较于现有柱塞泵效率的理论计算公式，所建立的非线性回归模型和BP神经网络模型均能显著提高柱塞泵效率的预测精度，两种方法的效率平均相对误差分别从8.89%提高到1.4%和0.62%。
Aiming at the problem of large errors in the theoretical formula and experimental results of axial piston pump efficiency under different working conditions, a pump efficiency calculation method based on machine learning is proposed. Firstly, a nonlinear regression model based on axial piston pump efficiency is established, and the validity of the regression model is verified by the significance test; subsequently, a set of piston pump efficiency prediction model based on BP neural network is designed, and the prediction model is trained and verified based on the experimental data; finally, the prediction accuracies of the two models are evaluated respectively. The results show that, compared with the existing theoretical formula of piston pump efficiency under the conditions of variable pressure, speed and flow rate, the established nonlinear regression model and BP neural network model can significantly improve the prediction accuracy of the piston pump efficiency, and the average relative errors of the efficiency of the two methods are improved from 8.89% to 1.4% and 0.62%, respectively.
