Open AccessRotor Fault Detection in Squirrel Cage Induction Motors using MCSA and DWT Techniques
Author(s) -
Noureddine Bessous,
Salim Sbaa,
Rémus Pusca,
Raphaël Romary
Publication year - 2021
Publication title -
algerian journal of signals and systems
Language(s) - English
Resource type - Journals
eISSN - 2676-1548
pISSN - 2543-3792
DOI - 10.51485/ajss.v6i1.7
Subject(s) - squirrel cage rotor , rotor (electric) , stator , induction motor , fault (geology) , discrete wavelet transform , fast fourier transform , fault detection and isolation , computer science , discrete fourier transform (general) , harmonics , control theory (sociology) , engineering , fourier transform , wavelet , artificial intelligence , algorithm , short time fourier transform , wavelet transform , mathematics , fourier analysis , electrical engineering , mathematical analysis , control (management) , voltage , seismology , geology , actuator
This article presents the fault detection of broken rotor bar (BRB) faults in squirrel cage induction motors (SCIMs). This work applied two diagnostic methods on stator current signal. It is necessary to verify the machine health to avoid any catastrophic damage. The first method uses the fast Fourier transform (FFT) which is generally called motor current signature analysis (MCSA). We carefully verified the spectral content of the stator current to detect BRB fault. The new harmonics allows us to take a good decision about BRB fault. The second method is based on the discrete wavelet transform (DWT). This technique is widely used in the diagnosis field of rotating machinery. According to DWT method, we used the mean square error (MSE) as a good indicator. An experimental test with different conditions of the induction motor has been performed. The experimental results have been exploited using MCSA and DWT methods to achieve a good decision.