Comparison of wavelet-functions for induction-motor rotor fault detection based on the hybrid “Time Synchronous Averaging - Discrete Wavelet Transform” approach

Early fault detection of the induction machine is necessary in order to guarantee its stability and high performance. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the low load case. To overcome this drawback, an efficient and new method to diagnose the induction-motor rotor fault based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform is presented. However, there are different types of the wavelet function that can be used for signal decomposition. This paper intends to investigate the ability of different types of wavelet functions for early rotor fault detection. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results indicate that the reliability of the fault detection depends on the type of wavelet function applied for decomposition of the signal.