ROLLING ELEMENT BEARINGS FAULT CLASSIFICATION BASED ON SVM AND FEATURE EVALUATION

被引：2

作者：

Sui, Wen-Tao ^{[1
,2
]}

Zhang, Dan ^{[3
]}

机构：

[1] Shandong Univ Technol, Sch Mech Engn, Zibo 255049, Peoples R China

[2] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China

[3] Shandong Univ Technol, Sch Elect & Elect Engn, Zibo 255049, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Fault diagnosis; Support vector machine; Feature evaluation; SUPPORT VECTOR MACHINE;

D O I：

10.1109/ICMLC.2009.5212574

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new method of fault diagnosis based on Support vector machine (SVM) and feature evaluation is presented. Feature evaluation based on class separability criterion is discussed in this paper. A multi-fault SVM classifier based on binary classifier is constructed for bearing faults. Compared with the Artificial Neural Network based method, the SVM based method has desirable advantages. Experiment shows that the algorithm is able to reliably recognize different fault categories. Therefore, it is a promising approach to fault diagnosis of rotating machinery.

引用

页码：450 / +

页数：2

共 8 条

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