Open AccessA new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis
Author(s) -
Defdaf Mabrouk,
Berrabah Fouad,
Chebabhi Ali,
Cherif Bilal Djamal Eddine
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12807
Subject(s) - kurtosis , rotor (electric) , fault (geology) , induction motor , artificial neural network , squirrel cage rotor , wavelet , control theory (sociology) , vibration , engineering , bar (unit) , shock (circulatory) , algorithm , computer science , pattern recognition (psychology) , artificial intelligence , mathematics , acoustics , physics , statistics , control (management) , voltage , meteorology , electrical engineering , mechanical engineering , medicine , seismology , geology
Summary The main objective of this article is to contribute the automatic fault diagnosis of broken rotor bars in three‐phase squirrel‐cage induction motor using vibration analysis. In fact, two approaches are combined to do so, based on signal processing technique and artificial intelligence technique. The first technique is based on discrete wavelet transform ( DWT ) to detect the harmonics that characterize this fault, using the Daubechies wavelet vibration analysis according to three axes ( X , Y , Z ). This application permits having the approximation mode function and the details ( recd ). To exact choice of reconstruction details which contains the information of the broken rotor bars faults, two statistical studies based on the root mean square values ( RMS ) and Kurtosis shock factor calculation are carried out for each ( recd ). The choice of ( recd ) is conditioned by ( RMS ) and Kurtosis values as: RMS recd1 < RMS recd2 and Kurtosis recd1 > Kurtosis recd2 . Experimental results showed that ( recd 1 and recd 2 ) satisfied the condition set for ( RMS ) and Kurtosis values. At the end of first technique, a spectral envelop of recd 1 is adopted to detect the broken rotor bars fault and the second technique based on artificial neural network ( ANN ) is used to identify the number of broken rotor bars. The characteristics of features used as input variables of ANN are the RMS of recd 1 and recd 2 , and the Kurtosis shock factor of recd 1 and recd 2 . The experimental results demonstrated the high efficiency of the proposed method with rotor broken bars fault classification rate of 98.66%.