Support vector machine classifier for diagnosis in electrical machines: Application to broken bar

Authors:
Dragan Matić;Filip Kulić;Manuel Pineda-Sánchez;Ilija Kamenko
Affiliations:
Faculty of Technical Science, University Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia;Faculty of Technical Science, University Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia;Instituto de Ingeniería Energética, Universidad Politecnica de Valencia, Camino de Vera, s/n, 46022 Valencia, Spain;Faculty of Technical Science, University Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 7
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Abstract

This paper presents a support vector machine classifier for broken bar detection in electrical induction machine. It is a reliable online method, which has high robustness to load variations and changing operating conditions. The phase current is only physical value to be measured. The steady state current is analyzed for broken bar fault via motor current signature analysis technique based on Hilbert transform. A two dimensional feature space is proposed. The features are: magnitude and frequency of characteristic peak extracted from spectrum of Hilbert transform series of the phase current. For classification task support vector machine is used due to its good robustness and generalization performances. A comparative analysis of linear, Gaussian and quadratic kernel function versus error rate and number of support vectors is done. The proposed classifier successfully detects a broken bar in various operational situations. The proposed method is sufficiently accurate, fast, and robust to load changes, which makes it suitable for use in real-time online applications in industrial drives.