IBRAHIM Chouidira
إبراهيم شويديرة
ibrahim.chouidira@univ-msila.dz
0666178274
- General Secretariat -- Faculty of Technology
- Faculty of Technology - Administrative Staff
- Grade Employee
About Me
Location
Msila, Msila
Msila, ALGERIA
Code RFIDE- 1972-06-14 00:00:00
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IBRAHIM Chouidira birthday
- 2019
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2019
Continuous Wavelet Technique for Detection of Broken Bar Faults in Induction Machine
The purpose of this study is diagnosis the fault of induction machine, through detection of broken bars by stator current analysis. Therefor we present a multi-winding model for the simulation of faults as part of the fault detection study, and test the behavior of the machine in the healthy and faulty state. In this paper it is focus the signal processing technique for detecting defects. This technique is based on continuous wavelet transform (CWT) to detect and locate defects, by using the multi-level decomposition for the detection of defects and the location of the broken bars at the rotor. The findings of this research results show the importance of this technique for the analysis of fault signatures in healthy and defective cases.
Citation
Djalal Eddine KHODJA , IBRAHIM Chouidira , Salim CHAKROUNE , , (2019), Continuous Wavelet Technique for Detection of Broken Bar Faults in Induction Machine, Traitement du Signal, Vol:36, Issue:2, pages:171-176, International Information and Engineering Technology Association
- 2019
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2019
Induction Machine Faults Detection and Localization by Neural Networks Methods
The objective of this study is to present artificial intelligence (AI) technique for detection and localization of fault in induction machine fault, through a multi-winding model for the simulation of four adjacent broken bars and three-phase model for the simulation of shortcircuit between turns. In this work, it was found that the application of artificial neural networks (ANN) based on Root mean square values (RMS) plays a big role for fault detection and localization. The simulation and obtained results indicate that ANN is able to detect the faulty with high accuracy.
Citation
Djalal Eddine KHODJA , IBRAHIM Chouidira , Salim CHAKROUNE , , (2019), Induction Machine Faults Detection and Localization by Neural Networks Methods, Revue d'Intelligence Artificielle, Vol:33, Issue:6, pages:427-434, International Information and Engineering Technology Association