Mine Fan Fault Diagnosis Based on EMD and SVM

被引：2

作者：

Leng, Junfa ^{[1
]}

Jing, Shuangxi ^{[1
]}

机构：

[1] Henan Polytech Univ, Sch Mech & Power Engn, Jiaozuo 454003, Henan, Peoples R China

来源：

ADVANCED RESEARCH ON MECHANICAL ENGINEERING, INDUSTRY AND MANUFACTURING ENGINEERING, PTS 1 AND 2 | 2011年 / 63-64卷

关键词：

EMD; SVM; Fault Feature Extraction; Mine Fan; EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINES;

D O I：

10.4028/www.scientific.net/AMM.63-64.449

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this research, a new method based on EMD and SVM for mine fan fault diagnosis is introduced. With EMD, fault feature can be extracted quickly and accurately, and taken as the input samples for SVM with the outstanding non-linear pattern classification performances. 5 two-class SVM classifiers are designed in order to classify and diagnosis 5 typical fault patterns of mine fan. The result of this research shows that the integrative method of EMD and SVM is very fit for the intelligent diagnosis and fault patterns recognition, and it will lead to the possible development of an automated and on-line mine fan condition monitoring and diagnostic system.

引用

页码：449 / 452

页数：4

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