Bearing Fault Detection in Adjustable Speed Drives via a Support Vector Machine with Feature Selection using a Genetic Algorithm

Abstract

This paper presents a novel method to detect bearing defects in adjustable speed drives (ASD's). The harmonics in pulse-width-modulation (PWM) input voltage waveforms and EMI noise in ASD systems make bearing fault detection more difficult. The proposed method accomplishes bearing fault detection in ASD's by combining motor current signature analysis (MCSA), wavelet packet decomposition (WPD), a genetic algorithm (GA), and a support vector machine (SVM). The SVM in conjunction with the GA is applied to the rms values of the wavelet packet coefficients to obtain significant wavelet packet nodes which produce optimal classifiers for classifying both healthy and defective bearings in ASD systems.