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《China Mechanical Engineering》 2016-01
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Fault Diagnosis of Rotating Machinery Based on Laplacian Support Vector Machines

Hao Tengfei;Chen Guo;Nanjing University of Aeronautics and Astronautics;  
In the intelligent fault diagnosis of rotating machinery,collecting a large number of data was relatively easy,but giving all collected data a label was often difficult.Aiming at this situation,an intelligent fault diagnosis approach for rotating machinery was proposed based on Laplacian support vector machines(LapSVM).The diagnosis example of rolling bearings shows that when the number of labeled data is limited,compared with the SVM that uses only labeled data for learning,the fault diagnosis approach based on LapSVM can improve the accuracy of fault diagnosis significantly by using a large amount of unlabeled data together with labeled data for learning.
【Fund】: 国家自然科学基金资助项目(61179057)
【CateGory Index】: TH165.3;TP18
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