Rotor fault diagnosis method based on kernel function approximation

被引：0

作者：

School of Automotive Engineering, South China University of Technology, Guangzhou 510640, China ^{[1
]}

不详 ^{[2
]}

机构：

[1] School of Automotive Engineering, South China University of Technology

[2] School of Mechanical Science and Engineering, Huazhong University of Science and Technology

来源：

Jixie Gongcheng Xuebao | 2006年 / 9卷 / 76-82期

关键词：

Fault classification; Feature selection; Kernel function; Rotor;

D O I：

10.3901/JME.2006.09.076

中图分类号：

学科分类号：

摘要：

Kernel function approximation is investigated together with some applications in mechanical fault diagnosis, and an approach to rotor fault classification based on feature samples selection is presented. The integral operator kernel functions is used to realize the nonlinear map from the raw feature space of rotor vibration signals to high dimensional feature space, where appropriate feature samples are selected to classify three kinds of rotor faults: rotor crack, rotor unbalance and rotor rub. The quantity of selected samples is much less than that of whole sample sets, which has quickly expedited the computation process. The classification result of KFA is compared with that of SVM. It can be seen that the classification accuracy of KFA is fairly as well as that of SVM, and KFA is or even better than SVM in terms of computation load.

引用

页码：76 / 82

页数：6

共 4 条

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[2]

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[3]

Mika S., Smola A., Scholkopf B., An improved training algorithm for kernel fisher discriminants, Proceedings AISTATS, 13, pp. 98-104, (2001)

[4]

Baudat G., Anouar F., Kernel-based methods and function approximation, International Joint Conference on Neural Networks (IJCNN 2001), pp. 1244-1249, (2001)

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