A Comparative Study between the adaptive wavelet transform and DWT Methods Applied to a Outer Raceway Fault Detection in Induction Motors based on the Frequencies Analysis

This paper presents a new application to diagnose the outer race fault in induction machines based on the three-step nonlinear lifting scheme. The wavelet transform is a powerful and complex tool in the context of diagnosis. The discovery of the lifting schemes structure make a wavelet filters simple, rapid and reversible. This method (lifting) is generally used by researchers in the field of image processing. However we are going in this study to use in order to see its effectiveness in the field of diagnosis in electrical machine in induction motors. In addition, we will exploit the experimental results. This study analyzes the stator current of an asynchronous motor for two conditions: the first is a data acquisition of a healthy machine. The second is a defective machine of an outer race fault and inner race fault. The objective of this work focuses particularly on the frequency analysis of the signal indicators of defects. Moreover, it remains necessary to develop filters for the detection, isolation and estimation of these defects by a certain number of diagnostic methods, techniques and to establish selection criteria for their use. For these reasons, this work compares the three-step nonlinear lifting scheme with the MCA-DWT current analysis method to access a valuable decision based on the experimental results.