Wavelet transform and least square support vector machine for mechanical fault detection of an alternator using vibration signal

Abstract

This article deals with fault detection of an alternator based on vibration signals using wavelet transform and least square support vector machine. Firstly, the noise in the vibration signal is removed using wavelet denoising. The denoised signals are then analysed using discrete wavelet transform with Daubechies mother wavelet. Several statistical features are then extracted from discrete wavelet transform coefficients of the signals. Finally, least square support vector machine is employed to detect and classify the different alternator conditions. The results show that the detection accuracy reached 90.48%. Hence, the proposed procedure is capable of detecting the alternator faults, and thus can be used for practical applications.