Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM

被引：3

作者：

Li, Yun ^{[1
]}

Gao, Yan ^{[1
]}

Guo, Jun

Yu, Xianjun ^{[1
]}

机构：

[1] Kunming Univ Sci & Technol, Kunming 650500, Yunnan, Peoples R China

来源：

ADVANCES IN CHEMICAL, MATERIAL AND METALLURGICAL ENGINEERING, PTS 1-5 | 2013年 / 634-638卷

关键词：

diagnostics; bearing fault; spectral kurtosis; genetic algorithm; support vector machine;

D O I：

10.4028/www.scientific.net/AMR.634-638.3958

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.

引用

页码：3958 / 3961

页数：4

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