Title :
Fault diagnostics of an electrical machine with multiple support vector classifiers
Author :
Poyhonen, Sanna ; Negrea, Marian ; Arkkio, Antero ; Hyotyniemi, Heikki ; Koivo, Heikki
Author_Institution :
Dept. of Autom. & Syst. Technol., Helsinki Univ. of Technol., Finland
Abstract :
Support vector machine (SVM) based classification is applied to fault diagnostics of an electrical machine. Numerical magnetic field analysis is used to provide virtual measurement data from healthy and faulty operations of an electric machine. Power spectra estimates of a stator line current of the motor are calculated with Welch´s method, and SVMs are applied to distinguish the healthy spectrum from faulty spectra. Multiple SVMs are combined with a majority voting approach to reconstruct the final classification decision.
Keywords :
electric motors; fault diagnosis; finite element analysis; magnetic fields; neural nets; pattern classification; stators; Welch method; electric motors; fault diagnostics; finite element analysis; magnetic field analysis; majority voting; pattern classification; power spectra estimates; support vector machine; Condition monitoring; Laboratories; Magnetic analysis; Magnetic field measurement; Neural networks; Statistical learning; Stators; Support vector machine classification; Support vector machines; Voting;
Conference_Titel :
Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on
Print_ISBN :
0-7803-7620-X
DOI :
10.1109/ISIC.2002.1157792