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《Electric Drive Automation》 2009-06
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Fault diagnosis for locomotive bearings based on least squares support vector machine

LEI Ye,JIANG Zi-yun (School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)  
For the problems of neural network for fault diagnosis,a method of fault diagnosis for locomotive bearing based on least squares support vector machine is proposed.The multi-class fault classifier is built based on least squares support vector machine.The inputing feature vectors are classed by the classifier and the function of classifier is completed.The simulation result not noly shows that the generalized ability of least squares support vector is better than neural network in the case of small samples but also presents obvious advantages for fault diagnosis in recognition accuracy and anti-disturbance ability.
【CateGory Index】: U269.32
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