Diagnosis of bearing defects in induction motors by fuzzy-neighborhood density-based clustering

被引：16

作者：

Farajzadeh-Zanjani, M. ^{[1
]}

Razavi-Far, R. ^{[1
]}

Saif, M. ^{[1
]}

Zarei, J. ^{[2
]}

Palade, V. ^{[3
]}

机构：

[1] Univ Windsor, Dept Elect & Comp Engn, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada

[2] Shiraz Univ Technol, Dept Elect Engn, Shiraz, Iran

[3] Coventry Univ, Fac Engn & Comp, Coventry, W Midlands, England

来源：

2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2015年

关键词：

FAULT-DIAGNOSIS; VIBRATION; ALGORITHM; MACHINE;

D O I：

10.1109/ICMLA.2015.114

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. The proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. The proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. This is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.

引用

页码：935 / 940

页数：6

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