Support vector machines and its applications in machine fault diagnosis

被引：0

作者：

Yuan, Sheng-Fa ^{[1
,2
]}

Chu, Fu-Lei ^{[1
]}

机构：

[1] Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China

[2] School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Jiangxi 341000, China

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2007年 / 26卷 / 11期

关键词：

Artificial intelligence - Failure analysis - Learning systems - Optimization;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimization principle. It exhibits good generalization when fault samples are few. Research progress on support vector machines and its applications in machine fault diagnosis in recent years are introduced. Characteristics of the SVM and the unsolved problems are discussed. The prospect on the research of SVM in the field of machine fault diagnosis is presented.

引用

页码：29 / 35

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