Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network
Internal leakage is the most common failure of hydraulic cylinder; when it increases, it decreases volumetric efficiency, pressure and speed of the hydraulic cylinder, and can seriously affect the normal operation of the hydraulic cylinder, so it is important to measure it, especially to measure it...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/9/2159 |
| _version_ | 1818037372487467008 |
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| author | Yuan Guo Yinchuan Zeng Liandong Fu Xinyuan Chen |
| author_facet | Yuan Guo Yinchuan Zeng Liandong Fu Xinyuan Chen |
| author_sort | Yuan Guo |
| collection | DOAJ |
| description | Internal leakage is the most common failure of hydraulic cylinder; when it increases, it decreases volumetric efficiency, pressure and speed of the hydraulic cylinder, and can seriously affect the normal operation of the hydraulic cylinder, so it is important to measure it, especially to measure it online. Firstly, the principle of internal leakage online measurement is proposed, including the online measurement system, the fixed mode of the strain gauge and the mathematical model of the flow-strain signal conversion. Secondly, an experimental system is established to collect internal leakages and strain values, and the data is processed. Finally, the convolutional neural network (CNN), BP neural network (BPNN), Radial Basis Function Network (RBF), and Support Vector Regression (SVR) are used to predict the hydraulic cylinder leakage; the comparison of experimental results show that the CNN has high accuracy and high efficiency. This study provides a new idea for online measurement of small flow on other hydraulic components. |
| first_indexed | 2024-12-10T07:25:48Z |
| format | Article |
| id | doaj.art-09a51336f4df4922a04a08eefeb2103b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:25:48Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-09a51336f4df4922a04a08eefeb2103b2022-12-22T01:57:42ZengMDPI AGSensors1424-82202019-05-01199215910.3390/s19092159s19092159Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural NetworkYuan Guo0Yinchuan Zeng1Liandong Fu2Xinyuan Chen3Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaHubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaHubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaInternal leakage is the most common failure of hydraulic cylinder; when it increases, it decreases volumetric efficiency, pressure and speed of the hydraulic cylinder, and can seriously affect the normal operation of the hydraulic cylinder, so it is important to measure it, especially to measure it online. Firstly, the principle of internal leakage online measurement is proposed, including the online measurement system, the fixed mode of the strain gauge and the mathematical model of the flow-strain signal conversion. Secondly, an experimental system is established to collect internal leakages and strain values, and the data is processed. Finally, the convolutional neural network (CNN), BP neural network (BPNN), Radial Basis Function Network (RBF), and Support Vector Regression (SVR) are used to predict the hydraulic cylinder leakage; the comparison of experimental results show that the CNN has high accuracy and high efficiency. This study provides a new idea for online measurement of small flow on other hydraulic components.https://www.mdpi.com/1424-8220/19/9/2159hydraulic cylinderinternal leakage online measurementstrain gaugeconvolutional neural network |
| spellingShingle | Yuan Guo Yinchuan Zeng Liandong Fu Xinyuan Chen Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network Sensors hydraulic cylinder internal leakage online measurement strain gauge convolutional neural network |
| title | Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network |
| title_full | Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network |
| title_fullStr | Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network |
| title_full_unstemmed | Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network |
| title_short | Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network |
| title_sort | modeling and experimental study for online measurement of hydraulic cylinder micro leakage based on convolutional neural network |
| topic | hydraulic cylinder internal leakage online measurement strain gauge convolutional neural network |
| url | https://www.mdpi.com/1424-8220/19/9/2159 |
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