基于 VMD 和 PSO-BP 的重型拖拉机 HMCVT 传动效率预测研究

Kai Lu, Jing Liang, Mengnan Liu, Zhixiong Lu, Jinzhong Shi, Pengfei Xing, Lin Wang
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摘要

传动效率是液力机械无级变速器(HMCVT)的关键特性,关系到重型拖拉机的性能。预测 HMCVT 变速箱的传动效率有利于在重型拖拉机运行过程中实时调整传动比,从而获得更好的性能。针对 HMCVT 传动效率预测中存在的方法不准确、精度低、噪声大等问题,本文提出了一种基于变分模式分解(VMD)、粒子群优化(PSO)和反向传播(BP)神经网络的方法,以提高传动效率预测的质量。首先,建立了一个简单的理论模型,以获得传输效率的影响因素。然后,根据这些因素,在台架上进行了多种条件下的传输效率测试,并利用偏最小二乘法(PLS)划分了各因素对传输效率的影响程度。最后,使用 VMD 方法对测试数据进行去噪处理,并利用 PSO 方法建立改进后的 BP 模型对处理后的数据进行预测。结果表明,HMCVT 的传动效率受输出转速的影响最大,其次是功率,而受输入转速的影响最小。VMD 方法能从原始数据中准确提取有效信号和噪声信号,并重建信号,降低噪声比例。在三种条件下,PSO-BP 模型的预测回归精度分别比 BP 模型高 7.02%、7.88% 和 9.26%。在三个预测实验中,PSO-BP 模型的 MAE、MAPE 和 RMSE 的最大差异分别为 0.002、0.463% 和 0.004,比 BP 模型分别低 0.006、0.796% 和 0.003。
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Research on Transmission Efficiency Prediction of Heavy-Duty Tractors HMCVT Based on VMD and PSO–BP
Transmission efficiency is a key characteristic of Hydro-mechanical Continuously Variable Transmission (HMCVT), which is related to the performance of heavy-duty tractors. Predicting the HMCVT transmission efficiency is beneficial for the real-time adjustment of transmission ratio during heavy-duty tractor operations, so as to obtain better performance. Aiming at the problems of accurate method, low accuracy, and high noise in the prediction of HMCVT transmission efficiency, this paper proposes a method based on Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Back Propagation (BP) neural networks to improve the quality of transmission efficiency prediction. Firstly, a simple theoretical model was established to obtain the influencing factors of transmission efficiency. Then, based on these factors, the transmission efficiency was tested on the bench under multiple conditions and the influence degree of each factor on transmission efficiency was divided using Partial Least Squares (PLS) method. Finally, the VMD method was used to denoise the test data, and a BP model, which was improved using the PSO method, was established to predict the processed data. The results showed that transmission efficiency of HMCVT is most affected by output speed, followed by power, and least by input speed. The VMD method can accurately extract effective signals and noise signals from the original data, and reconstruct signals, reducing the noise proportion. Using three conditions, the prediction regression accuracy of the PSO–BP model is 7.02%, 7.88%, and 9.26% higher than that of the BP model, respectively. In the three prediction experiments, the maximum differences in the MAE, the MAPE, and the RMSE of the PSO–BP model are 0.002, 0.463%, and 0.004, respectively, which are 0.006, 0.796%, and 0.003 lower than those of the BP model.
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