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Modeling and Predictive Analysis of Small Internal Leakage of Hydraulic Cylinder Based on Neural Network

Author

Listed:
  • Yuan Guo

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    These authors contributed equally to this work.)

  • Ge Xiong

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Liangcai Zeng

    (Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
    These authors contributed equally to this work.)

  • Qingfeng Li

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    These authors contributed equally to this work.)

Abstract

The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network is the most accurate. This research provides scientific and technical support for measuring and predicting small leaks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2456-:d:543380
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.

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