Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

被引：61

作者：

Rojas, Alfonso ^{[1
]}

Nandi, Asoke K. ^{[1
]}

机构：

[1] Univ Liverpool, Dept Elect Engn & Elect, Signal Proc & Commun Grp, Liverpool L69 3GJ, Merseyside, England

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2006年 / 20卷 / 07期

关键词：

support vector machines; condition monitoring;

D O I：

10.1016/j.ymssp.2005.05.002

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

This paper studies the application of support vector machines (SVMs) to the detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the sequential minimal optimization (SMO) algorithm. In this paper, a mechanism for selecting adequate training parameters is proposed. This proposal makes the classification procedure fast and effective. Various scenarios are examined using two sets of vibration data, and the results are compared with those available in the literature that are relevant to this investigation. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：1523 / 1536

页数：14

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