Data-Based Flow Rate Prediction Models for Independent Metering Hydraulic Valve

Wenbin Su, Wei Ren (), Hui Sun, Canjie Liu, Xuhao Lu, Yingli Hua, Hongbo Wei and Han Jia
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Wenbin Su: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China
Wei Ren: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China
Hui Sun: Jiangsu Advanced Construction Machinery Innovation Center Ltd., Xuzhou 221000, China
Canjie Liu: Jiangsu Advanced Construction Machinery Innovation Center Ltd., Xuzhou 221000, China
Xuhao Lu: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China
Yingli Hua: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China
Hongbo Wei: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China
Han Jia: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710000, China

Energies, 2022, vol. 15, issue 20, 1-12

Abstract: Accurate valve flow rate prediction is essential for the flow control process of independent metering (IM) hydraulic valve. Traditional estimation methods are difficult to meet the high-precision requirements under the restricted space of the valve. Thus data-based flow rate prediction method for IM valve has been proposed in this study. We took the four-spool IM valve as the research object, and carried out the IM valve experiments to generate labeled data. Picking up the post-valve pressure and valve opening as input, we developed and compared eight different data-based estimation models, including machine learning and deep learning. The results indicated that the SVR and DNN with three hidden layers performed better than others on the whole dataset in the trade-off of overfitting and precision. And MAPE of these two models was close to 4%. This study provides further guidelines on high-precision flow rate prediction of hydraulic valves, and has definite application value for development of digital and intelligent hydraulic systems in construction machinery.

Keywords: independent metering hydraulic valve; valve flow rate prediction; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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