Failure Diagnosis of Hydraulic Motor in Amphibious Assault Vehicles Based on Support Vector Machine (SVM)

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Abstract:

Hydraulic motor is one of the elements with a high occurrence rate of failure in hydraulic system of amphibious assault vehicles. When the motor leakage reaches the limit value allowed, the motor should be renewed or overhauled. Through an analysis of the influencing factors that affect hydraulic motor leakage of amphibious assault vehicles, this article establishes a support vector regression model for motor leakage and gets a maximum relative error of 4.29% between the fitted value and the measured value of motor leakage, which offers more reliable evidence for scientific determination of renewal period or overhaul period of hydraulic motor

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Periodical:

Advanced Materials Research (Volumes 503-504)

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1545-1549

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April 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] DONG Yu-cai, HE Guo-liang. The Fault Diagnose of Advanced Amphibious Assault Vehicle Fan Pump Based on Partial Least Square Method, J, Equipment Manufacturing Technology, 6(2010), 1-3.

Google Scholar

[2] ZHANG Xiao-ping, LIU Gui-xiong, ZHOU Song-bin. Target localization based on LSSVR in wireless sensor networks, J. Optics and Precision Engineering, 2010, 18(2010), 2060-(2068).

Google Scholar

[3] LI Bo, GU Chong-shi, LI Zhi-lu, ZHANG Zhen-zhen, Monitoring model for dam seepage based on partial least-squares regression and partial least square support vector machine, J. Journal of Hydraulic Engineering, 39(2008), 1390-1394, 1400.

DOI: 10.1007/978-3-540-89465-0_316

Google Scholar

[4] SUN Lin, YANG Shi-yuan, WU De-hui, Prediction model for city water resources carrying capacity based on least squares support vector machine, J. Water Sciences and Engineering Technology, B10(2008), 34-37.

Google Scholar

[5] PANG Ming-bao, XIE Ling, HAO Ran, MA Ning, Predicting into Regional Logistics Volume Based on Partial Least-squares Support Vector Machines Regression, J. Journal of Hebei University of Technology, 37(2008), 91-96.

Google Scholar

[6] BAI Mao-jin, CHEN Gang, LIU Qing, ZHANG Zuo-peng, ZHANG Xue-jun, A new method of transient stability forecasting based on LS-SVR, J. Power System Protection and Control, 36(2008), 9-14.

Google Scholar

[7] XU Hong-zhong, YANG Lei, Prediction foundation pit deformation based on least squar support vector machine regression, J. Journal of Nanjing University of Technology, 29(2007):21-24.

Google Scholar

[8] LIU De-di, CHEN Xiao-hong, Model for Prediction of Saltwater Intrusions Based on Coupling of Support Vector Machine and Partial Least Square Method, J. Acta Scientiarum Naturalium Universitatis Sunyatseni, 46(2007), 89-92.

Google Scholar

[9] WANG Yong-sheng, LIU Wei-hua, YANG Li-bin, SUN Yang, Research on the Prediction of the Chaotic Time Series Based on Lease Square Support Vector Machine, J. Journal of Naval Aeronautical Engineering Institut, 24(2009):283-288.

Google Scholar