Support vector classification for fault diagnostics of an electrical machine

Author

Pöyhönen, Sanna ; Negrea, Marian ; Arkkio, Antero ; Hyötyniemi, Heikki ; Koivo, Heikki

Author_Institution

Lab. of Control Eng., Helsinki Univ. of Technol., Finland

Volume

2

fYear

2002

fDate

26-30 Aug. 2002

Firstpage

1719

Abstract

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 the 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.

Keywords

electric motors; electrical engineering computing; fault diagnosis; finite element analysis; learning automata; magnetic fields; signal classification; stators; SVC; Welch method; electrical machine; fault diagnostics; finite element analysis; motor stator current; numerical magnetic field analysis; power spectra estimates; support vector classification; virtual measurement data; Area measurement; Computational modeling; Electric variables measurement; Fault detection; Laboratories; Magnetic analysis; Magnetic field measurement; Signal processing; Static VAr compensators; Stators;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2002 6th International Conference on

Print_ISBN

0-7803-7488-6

Type

conf

DOI

10.1109/ICOSP.2002.1180133

Filename

1180133