DocumentCode :
3182806
Title :
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
Link To Document :
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