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

Highlights? System for broken bar detection for wide slip range. ? Detection based on measuring only a motor current. ? No need for mathematical models, classifier is trained on acquired data. ? Detection of broken bar at low slip, where classical broken bar detection classifiers are not applicable. ? Reliable, mobile, and cost effective system that can be successfully applied in real working environment. 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.

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