Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

被引:11
作者
Wang Hongjun [1 ,2 ]
Xu Xiaoli [1 ,2 ]
Rosen B G [1 ,3 ]
机构
[1] Key Laboratory of Modern Measurement & Control Technology, Beijing Information Science & Technology University
[2] School of Mechanic and Electric Engineering, Beijing Information Science&Technology University
[3] Halmstad University
关键词
D O I
10.19650/j.cnki.cjsi.2014.s2.039
中图分类号
TH165.3 [];
学科分类号
摘要
Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.
引用
收藏
页码:210 / 214
页数:5
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