{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T20:48:21Z","timestamp":1772657301685,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:00:00Z","timestamp":1557360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51475338"],"award-info":[{"award-number":["51475338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2014CFA013"],"award-info":[{"award-number":["2014CFA013"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19092159","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"2159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Yuan","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Yinchuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Liandong","family":"Fu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Xinyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gong, W., Chen, H., Zhang, Z., Zhang, M., Wang, R., Guan, C., and Wang, Q. (2019). A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion. Sensors, 19.","DOI":"10.3390\/s19071693"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/14399776.2005.10781210","article-title":"Hydraulic Actuator Leakage Fault Detection using Extended Kalman Filter","volume":"6","author":"An","year":"2005","journal-title":"Int. J. Fluid Power"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yang, S.S., Mohamed, H.A.F., Moghavvemi, M., and Goh, Y.H. (2008, January 21\u201324). Leakage detection via model based method. Proceedings of the IEEE Conference on Robotics, Automation and Mechatronics, Chengdu, China.","DOI":"10.1109\/RAMECH.2008.4681449"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A new convolutional neural network-based data-driven fault diagnosis method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4374","DOI":"10.1109\/TIE.2010.2095396","article-title":"A wavelet-based approach for external leakage detection and isolation from internal leakage in valve-controlled hydraulic actuators","volume":"58","author":"Goharrizi","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/TIE.2009.2032198","article-title":"A wavelet-based approach to internal seal damage diagnosis in hydraulic actuators","volume":"57","author":"Goharrizi","year":"2010","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.compfluid.2014.09.034","article-title":"Experimental study of hydraulic cylinder leakage and fault feature extraction based on wavelet packet analysis","volume":"106","author":"Zhao","year":"2015","journal-title":"Comput. Fluids"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/14399776.2010.10780998","article-title":"A Wavelet-Based Approach for Diagnosis of Internal Leakage in Hydraulic Actuators using On-Line Measurements","volume":"11","author":"Goharrizi","year":"2010","journal-title":"Int. J. Fluid Power"},{"key":"ref_9","first-page":"368","article-title":"Internal Leakage Detection in Hydraulic Actuators Using Empirical Mode Decomposition and Hilbert Spectrum","volume":"61","author":"Goharrizi","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/14399776.2013.10781074","article-title":"Application of Fast Fourier and Wavelet Transforms Towards Actuator Leakage Diagnosis: A Comparative Study","volume":"14","author":"Goharrizi","year":"2013","journal-title":"Int. J. Fluid Power"},{"key":"ref_11","first-page":"3709","article-title":"Internal leakage fault diagnosis of hydraulic cylinder using PCA and BP network","volume":"42","author":"Tang","year":"2011","journal-title":"J. Cent. South Univ. (Sci. Technol.)"},{"key":"ref_12","first-page":"38","article-title":"Internal leakage fault diagnosis approach of hydraulic cylinder using LMBP neural network","volume":"1","author":"Zang","year":"2013","journal-title":"J. Tianjin Norm. Univ. (Nat. Sci. Ed.)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2932","DOI":"10.1002\/cem.2932","article-title":"Independent component analysis based on data-driven reconstruction of multi-fault diagnosis","volume":"31","author":"Feng","year":"2017","journal-title":"J. Chemom."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1049\/iet-smt.2016.0423","article-title":"An intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder","volume":"11","author":"Xia","year":"2017","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Sigal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, X., Su, R., Lu, C., and Rui, X. (2015, January 28\u201330). Internal leakage detection for wind turbine hydraulic pitching system with computationally efficient adaptive asymmetric SVM. Proceedings of the 34th Chinese Control Conference (CCC), Hangzhou, China.","DOI":"10.1109\/ChiCC.2015.7260599"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, Q., Guo, B., and Lin, M. (2010, January 23\u201324). Differential pressure prediction in air leak detection using RBF Neural Network. Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, Sanya, China.","DOI":"10.1109\/AICI.2010.51"},{"key":"ref_18","first-page":"11","article-title":"Internal Leakage Detection of Hydraulic Cylinder Based on BP Neural Network","volume":"7","author":"Li","year":"2017","journal-title":"Chin. Hydraul. Pneum."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_20","first-page":"182","article-title":"Convolution Neural Network Based Internal Leakage Fault Diagnosis for Hydraulic Cylinders","volume":"45","author":"Ji","year":"2017","journal-title":"Mach. Tool Hydraul."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_22","unstructured":"Deng, J.H., Chen, X.Y., Wu, L., Guo, Y., Huang, F.X., and Zhan, C.C. (2015). Hydraulic Cylinder Capable of Automatically Monitoring Internal Leakage. (104963912 A), CN Patent."},{"key":"ref_23","unstructured":"Liu, H. (2011). Material Mechanics, Higher Education Press. [5th ed.]."},{"key":"ref_24","first-page":"8","article-title":"Design of Intelligent Strain Target Flowmeter","volume":"2","author":"Yang","year":"2004","journal-title":"Meas. Tech."},{"key":"ref_25","unstructured":"Sha, Y. (2016). Fluid Mechanics, China University of Science and Technology Press."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Boureau, Y.L., Roux, N.L., Bach, F., Ponce, J., and Lecun, Y. (2011, January 6\u201313). Ask the Locals: Multi-way Local Pooling for Image Recognition. Proceedings of the 13th International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126555"},{"key":"ref_29","unstructured":"Shan, J.H., Lv, Q., Zhang, S.L., Meng, R., and Wang, X.Y. (2017, December 26). Multi-SoftMax Convolution Neural Network and Its Application in the Diagnosis of Planetary Gearbox Complicated Faults. Available online: http:\/\/chinaxiv.org\/abs\/201712.00240."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, L., Xie, W., and Zhang, Y. (2019). Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery. Appl. Sci., 9.","DOI":"10.3390\/app9061085"},{"key":"ref_31","first-page":"62","article-title":"Fault Diagnosis Method of Transformer Based on Convolutional Neural Network","volume":"54","author":"Jia","year":"2017","journal-title":"Electr. Meas. Instrum."},{"key":"ref_32","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Neural Information Processing Systems Conference, Lake Tahoe, NV, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1162\/089976604773135104","article-title":"Are Loss Functions All the Same?","volume":"16","author":"Rosasco","year":"2004","journal-title":"Neural Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Netw."},{"key":"ref_35","unstructured":"Kurbiel, T., and Khaleghian, S. (2017). Training of Deep Neural Networks based on Distance Measures using RMSProp. arXiv."},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1080\/13614568.2013.832407","article-title":"Highly reliable state monitoring system for induction motors using dominant features in a two-dimension vibration signal","volume":"19","author":"Nguyen","year":"2013","journal-title":"New Rev. Hypermedia Multimedia"},{"key":"ref_38","unstructured":"Vapnik, V., and Vapnik, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_39","unstructured":"Cherkassky, V., and Mulier, F.M. (1998). Learning From Data: Concepts, Theory, and Methods, Wiley."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2159\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:50:35Z","timestamp":1760187035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,9]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19092159"],"URL":"https:\/\/doi.org\/10.3390\/s19092159","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,9]]}}}