Development of a Tractor Hydrostatic Transmission Efficiency Prediction Model Using Novel Hybrid Deep Kernel Learning and Residual Radial Basis Function Interpolator Model

Jin Kam Park, Oleksandr Yuhai, Jin Woong Lee, Yubin Cho and Joung Hwan Mun ()
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Jin Kam Park: Specialized Machinery and Robotics Group, Jeonbuk Technology Application Division (Specialized Machinery), Korea Institute of Industrial Technology, 119, Jipyeongseonsandan 3-gil, Baeksan-myeon, Gimje 54325, Republic of Korea
Oleksandr Yuhai: Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Jin Woong Lee: Specialized Machinery and Robotics Group, Jeonbuk Technology Application Division (Specialized Machinery), Korea Institute of Industrial Technology, 119, Jipyeongseonsandan 3-gil, Baeksan-myeon, Gimje 54325, Republic of Korea
Yubin Cho: Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Joung Hwan Mun: Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

Agriculture, 2025, vol. 15, issue 22, 1-27

Abstract: This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables (input shaft speed, HST ratio, and load), which were used as the training data. A hybrid prediction model combining deep kernel learning and a residual radial basis function surrogate was developed with hyperparameters optimized via Bayesian optimization. For performance verification, the proposed model was compared with Neural Network (NN), Random Forest, XGBoost, Gaussian Process (GP), and Support Vector Regressor (SVR) models trained using 27 samples. As a result, the proposed model achieved the highest prediction accuracy (R 2 = 0.93, MAPE = 5.94%, RMSE = 4.05). Process, SVM (Support Vector MA). These findings indicate that the proposed approach can be effectively used to predict the overall HST efficiency using minimal data, particularly in situations where experimental data collection is limited.

Keywords: hydrostatic transmission; efficiency; deep kernel learning; radial basis function; minimum data (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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