Fault diagnostics of an electrical machine with multiple support vector classifiers
- 26 June 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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. Author(s) Poyhonen, S. Dept. of Autom. & Syst. Technol., Helsinki Univ. of Technol., Finland Negrea, M. ; Arkkio, A. ; Hyotyniemi, H. ; Koivo, H.Keywords
This publication has 7 references indexed in Scilit:
- Support vector classification for fault diagnostics of an electrical machinePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Neural-network-based motor rolling bearing fault diagnosisIEEE Transactions on Industrial Electronics, 2000
- A review of induction motors signature analysis as a medium for faults detectionIEEE Transactions on Industrial Electronics, 2000
- An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsPublished by Cambridge University Press (CUP) ,2000
- Polynomial predictive filtering in control instrumentation: a reviewIEEE Transactions on Industrial Electronics, 1999
- Monitoring and diagnosis of rolling element bearings using artificial neural networksIEEE Transactions on Industrial Electronics, 1993
- Finite element analysis of cage induction motors fed by static frequency convertersIEEE Transactions on Magnetics, 1990