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《China Mechanical Engineering》 2016-24
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Fault Identification Method for Hydraulic Pumps Based on Multi-feature Fusion and Multiple Kernel Learning SVM

Liu Zhiqiang;Jiang Wanlu;Tan Wenzhen;Zhu Yong;Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control,Yanshan University;Key Laboratory of Advanced Forging & Stamping Technology and Science,Ministry of Education of China;Tangsteel High Strength Automotive Strip Co.,Ltd.;  
A hydraulic pump fault identification method was put forward based on multiple feature fusion and multiple kernel learning SVM.Firstly,the original signals were processed by the ensemble empirical mode decomposition.Then,the feature vectors of hydraulic pump faults were obtained by using the autoregressive model and the singular value decomposition.Through different types of features mapped by corresponding different kernel functions,the hydraulic pump working conditions and fault types might be finally identified by multiple kernel learning SVM.The experimental results show that the approach improves the accuracy of fault diagnosis significantly.
【Fund】: 国家自然科学基金资助项目(51475405);; 国家重点基础研究发展计划(973计划)资助项目(2014CB046405);; 河北省自然科学基金资助项目(E2013203161)
【CateGory Index】: TH137.51;TP181
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【Citations】
Chinese Journal Full-text Database 3 Hits
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【Co-citations】
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1 SHEN Jian;JIANG Yun;ZHANG Ya-nan;HU Xue-wei;College of Computer Science and Engineering,Northwest Normal University;;Novel Multi-scale Kernel SVM Method Based on Sample Weighting[J];计算机科学;2016-12
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