IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3320-d1118680.html
   My bibliography  Save this article

Artificial Intelligence Methods in Hydraulic System Design

Author

Listed:
  • Grzegorz Filo

    (Faculty of Mechanical Engineering, Cracow University of Technology, 31-864 Kraków, Poland)

Abstract

Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

Suggested Citation

  • Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3320-:d:1118680
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3320/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/8/3320/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maosen Xu & Guorui Zeng & Dazhuan Wu & Jiegang Mou & Jianfang Zhao & Shuihua Zheng & Bin Huang & Yun Ren, 2022. "Structural Optimization of Jet Fish Pump Design Based on a Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 15(11), pages 1-16, June.
    2. Yakut, Oguz, 2021. "Implementation of hydraulically driven barrel shooting control by utilizing artificial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1206-1223.
    3. Wei Han & Lingbo Nan & Min Su & Yu Chen & Rennian Li & Xuejing Zhang, 2019. "Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network," Energies, MDPI, vol. 12(14), pages 1-14, July.
    4. Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. 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.
    6. Junjie Wu & Xiaoxi Zhang, 2022. "Convolutional Neural Network Identification of Stall Flow Patterns in Pump–Turbine Runners," Energies, MDPI, vol. 15(15), pages 1-16, August.
    7. Ha Quang Man & Doan Huy Hien & Kieu Duy Thong & Bui Viet Dung & Nguyen Minh Hoa & Truong Khac Hoa & Nguyen Van Kieu & Pham Quy Ngoc, 2021. "Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam," Energies, MDPI, vol. 14(22), pages 1-21, November.
    8. Faried Makansi & Katharina Schmitz, 2022. "Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks," Energies, MDPI, vol. 15(17), pages 1-19, August.
    9. Stephen Ntiri Asomani & Jianping Yuan & Longyan Wang & Desmond Appiah & Kofi Asamoah Adu-Poku, 2020. "The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)," Energies, MDPI, vol. 13(9), pages 1-29, May.
    10. Yuan Guo & Ge Xiong & Liangcai Zeng & Qingfeng Li, 2021. "Modeling and Predictive Analysis of Small Internal Leakage of Hydraulic Cylinder Based on Neural Network," Energies, MDPI, vol. 14(9), pages 1-14, April.
    11. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    12. Jiapeng Yan & Huifang Kong & Zhihong Man, 2022. "Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles," Energies, MDPI, vol. 15(24), pages 1-17, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lixin Wei & Yu Zhang & Lili Ji & Lin Ye & Xuanchen Zhu & Jin Fu, 2022. "Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network," Energies, MDPI, vol. 15(16), pages 1-12, August.
    2. Jia Li & Xin Wang & Yue Wang & Wancheng Wang & Baibing Chen & Xiaolong Chen, 2020. "Effects of a Combination Impeller on the Flow Field and External Performance of an Aero-Fuel Centrifugal Pump," Energies, MDPI, vol. 13(4), pages 1-16, February.
    3. Jin, Rui & Li, Lei & Liang, Xiaoling & Zou, Xiang & Yang, Zeyuan & Ge, Shuzhi Sam & Huang, Haihong, 2024. "Energy-efficient design of the powertrain for mechanical-electro-hydraulic equipment via configuring multidimensional controllable variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    4. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Dai, Menghang & Liu, Zhiliang & Wang, Jinrui & Zuo, Mingjian, 2024. "Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    6. Joanna Fabis-Domagala & Mariusz Domagala, 2022. "A Concept of Risk Prioritization in FMEA of Fluid Power Components," Energies, MDPI, vol. 15(17), pages 1-14, August.
    7. Morabito, Alessandro & Vagnoni, Elena & Di Matteo, Mariano & Hendrick, Patrick, 2021. "Numerical investigation on the volute cutwater for pumps running in turbine mode," Renewable Energy, Elsevier, vol. 175(C), pages 807-824.
    8. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    9. Joanna Fabis-Domagala & Mariusz Domagala & Hassan Momeni, 2021. "A Concept of Risk Prioritization in FMEA Analysis for Fluid Power Systems," Energies, MDPI, vol. 14(20), pages 1-16, October.
    10. Hong, Jichao & Zhang, Tiezhu & Zhang, Zhen & Zhang, Hongxin, 2023. "Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio," Energy, Elsevier, vol. 278(C).
    11. Zhao, Shuaiyu & Duan, Yiling & Roy, Nitin & Zhang, Bin, 2024. "A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    12. Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
    13. Zhang, Yiming & Li, Jingxiang & Fei, Liangyu & Feng, Zhiyan & Gao, Jingzhou & Yan, Wenpeng & Zhao, Shengdun, 2023. "Operational performance estimation of vehicle electric coolant pump based on the ISSA-BP neural network," Energy, Elsevier, vol. 268(C).
    14. Ferreira Neto, Waldomiro Alves & Virgínio Cavalcante, Cristiano Alexandre & Do, Phuc, 2024. "Deep reinforcement learning for maintenance optimization of a scrap-based steel production line," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    15. Péter Koroncz & Zsanett Vizhányó & Márton Pál Farkas & Máté Kuncz & Péter Ács & Gábor Kocsis & Péter Mucsi & Anita Fedorné Szász & Ferenc Fedor & János Kovács, 2022. "Experimental Rock Characterisation of Upper Pannonian Sandstones from Szentes Geothermal Field, Hungary," Energies, MDPI, vol. 15(23), pages 1-22, December.
    16. Longyan Wang & Stephen Ntiri Asomani & Jianping Yuan & Desmond Appiah, 2020. "Geometrical Optimization of Pump-As-Turbine (PAT) Impellers for Enhancing Energy Efficiency with 1-D Theory," Energies, MDPI, vol. 13(16), pages 1-30, August.
    17. Li, Gang & Hu, Jiayao & Ding, Yaping & Tang, Aimin & Ao, Jiaxing & Hu, Dalong & Liu, Yang, 2024. "A novel method for fault diagnosis of fluid end of drilling pump under complex working conditions," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    18. Ji-Quan Wang & Hong-Yu Zhang & Hao-Hao Song & Pan-Li Zhang & Jin-Ling Bei, 2022. "Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    19. Chen, Xirui & Liu, Hui, 2025. "Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    20. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3320-:d:1118680. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.