Induction Motor Stator Fault Detection Using Discrete Wavelet Transform Based on Statistical Parameters

The aim of the paper is to identification the stator faults in induction motors. Stator fault affects other fault, especially in electrical systems such as the appearance of sparks on the rotor shaft, reducing rotor rotation, and increase in temperature which causes the stator to burn. Therefore, an early detection system is needed to determine stator fault. In this research, the identification stator fault conditions using current signal analysis. Furthermore, the current signal is extracted using Discrete Wavelet Transform (DWT) based on statistical parameters. The experimental results are the signal decomposition level from Detail Coefficient 1 (D1) to Detail Coefficient 5 (D5) and Approximation 5. The decomposition signal results in the Detail Coefficient 4 (D4) is a significant difference signal in stator normal condition and stator fault condition. The results of the signal Detail Coefficient 4 (D4) obtained are very difficult to identify. Therefore, the statistical values used are RMS, Kurtosis, and Variance which show the differences in the values of each stator condition. The experimental results can identify the normal stator condition and 5 to 60 turn fault stator windings.