Support vector classification for fault diagnostics of an electrical machine

被引：0

作者：

Pöyhönen, S ^{[1
]}

Negrea, M ^{[1
]}

Arkkio, A ^{[1
]}

Hyötyniemi, H ^{[1
]}

Koivo, H ^{[1
]}

机构：

[1] Helsinki Univ Technol, Lab Control Engn, Helsinki 02015, Finland

来源：

2002 6TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I AND II | 2002年

关键词：

support vector classification; fault diagnostics; electrical machine; finite element analysis;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support vector classification (SVC) is applied to fault diagnostics of an electrical machine. Numerical magnetic field analysis is used to provide virtual measurement data from healthy and faulty operation of an electrical machine. Power spectra estimates of a stator current of the motor are calculated with Welch's method, and SVC is applied to distinguish healthy spectrum from faulty spectra. Results are promising. Most of the faults can be classified correctly.

引用

页码：1719 / 1722

页数：4

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