Fault diagnosis of rotating machinery based on multi-class support vector machines

被引：94

作者：

Yang, BS ^{[1
]}

Han, T ^{[1
]}

Hwang, WW ^{[1
]}

机构：

[1] Pukyong Natl Univ, Sch Mech Engn, Pusan 608739, South Korea

来源：

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2005年 / 19卷 / 03期

关键词：

fault diagnosis; support vector machine; rotating machinery; multi-class classification;

D O I：

10.1007/BF02916133

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

引用

页码：846 / 859

页数：14

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