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

被引：0
|
作者：
Bo-Suk Yang
Tian Han
Won-Woo Hwang
机构：
[1] Pukyong National University,School of Mechanical Engineering
[2] Pukyong National University,School of Mechanical Engineering
来源：
Journal of Mechanical Science and Technology | 2005年 / 19卷
关键词：
Fault Diagnosis; Support Vector Machine; Rotating Machinery; Multi-Class Classification;
D O I：
暂无
中图分类号：
学科分类号：
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
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页码：846 / 859
页数：13
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