Diagnosis of bearing defects in induction motors using discrete wavelet transform

Abstract

The analysis of motor current signature analysis was used many years ago, but the fast Fourier transform (FFT) technique has some disadvantages under some conditions when the speed and the load torque are not constants. The FFT has problems due to a non-stationary signal if we must report accurately the frequency characteristics of the defects. Discrete wavelets transform (DWT) treats the non-stationary stator current signal, which becomes complex when it has noises. In this paper, a technique of de-noising signals is presented by the stator current based on a series of decomposition which are compared with respect to each other. We studied a normal bearings and bearings with outer and inner faults. The choice of the decomposition order was for: Daubechies, Symlets and Meyer. The limit point of determination of the levels number is presented. In addition, we look for informations about the basic defect signal on the energy stored in each level of decomposition. DWT has the ability to allow simultaneous time–frequency analysis, so it is an appropriate tool for studying transient phenomena and non-stationary signals.