Detection of Broken Rotor Bars and Stator Faults in Squirrel-Cage Induction Machine by Spectral Analysis

Author(s):  
O. Touhami ◽  
M. Fadel
Author(s):  
Souad Saadi Laribi ◽  
Azzedine Bendiabdellah

This chapter focuses on the monitoring and diagnosis of induction machine faults, particularly the broken rotor bars. The design of a system for monitoring, detecting, and locating incipient faults for different loads of the machine is achieved by the use of advanced intelligent techniques based on ANFIS-based neuro-fuzzy network. The knowledge base is based on indicators derived from the stator current spectral analysis of the machine which in addition has to detect and assess the number of faulty bars.


Author(s):  
Ilias Ouachtouk ◽  
Soumia El Hani ◽  
Said Guedira ◽  
Khalid Dahi ◽  
Lahbib Sadiki

2018 ◽  
Vol 3 (3) ◽  
pp. 143-150
Author(s):  
Abdelghani CHAHMI

This work is a part of the thematic of monitoring and fault diagnosis of the squirrel cage three-phase induction machine. The choice of this type of machine is justified by the growing success it has exhibited, mainly, in the electric drives with variable speed. Signal based detection methods are presented is validated in simulation. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.In this study, the proposed approach considers the value of rotor resistance as fixed for condition monitoring. This value in the diagnostic tools which one uses is not fixed contrary to the classical approaches of control of machine.


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