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

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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)

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.

Suggested Citation

  • Wenbin Su & Wei Ren & Hui Sun & Canjie Liu & Xuhao Lu & Yingli Hua & Hongbo Wei & Han Jia, 2022. "Data-Based Flow Rate Prediction Models for Independent Metering Hydraulic Valve," Energies, MDPI, vol. 15(20), pages 1-12, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7699-:d:946286
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    References listed on IDEAS

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    1. Fausto Valencia & Hugo Arcos & Franklin Quilumba, 2021. "Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis," Energies, MDPI, vol. 14(14), pages 1-27, July.
    2. Yubiao Sun & Qiankun Sun & Kan Qin, 2021. "Physics-Based Deep Learning for Flow Problems," Energies, MDPI, vol. 14(22), pages 1-24, November.
    3. N. Speybroeck, 2012. "Classification and regression trees," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(1), pages 243-246, February.
    4. Duwon Choi & Youngkuk An & Nankyu Lee & Jinil Park & Jonghwa Lee, 2020. "Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System," Energies, MDPI, vol. 13(20), pages 1-24, October.
    5. Do, Tri Cuong & Dang, Tri Dung & Dinh, Truong Quang & Ahn, Kyoung Kwan, 2021. "Developments in energy regeneration technologies for hydraulic excavators: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
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    Cited by:

    1. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.

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