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Diagnosis of Stator Turn to Turn Fault and Rotor Broken Bars Fault Using Neuro Fuzzy Inference System

Auteurs : Merabet hichem, BAHI Tahar, DRICI Djalel
Année : 2016
Domaine : Electrotechnique
Type : Communication
Conférence: 3rd International Conference on Automation, Control, Engineering and Computer Science (ACECS-2016)
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Mots clés : Induction machine, diagnosis, Detection, Neuro-Fuzzy inference system, modeling, Simulation.

Résumé :

With the growing of using the induction machines (IM) gained large importance and are being widely used as electromechanical system device regarding for theirrobustness, reliability, and simple design with well developed technologies. This work presents a reliable method for diagnosis and detection of rotor broken bars faults and stator short-circuit windings faults in induction machine. The detection faults is based on monitoring of the current signal. Also the calculation of the value of relative energy for each level of signal decomposition using package wavelet, which will be useful as data inputof adaptive Neuro-Fuzzy inference system (ANFIS). In this method, fuzzy logic is used to make decisions about the machine state. The adaptive Neuro-Fuzzy inference system is able to identify the IM bearing state with high precision. This technique is applied under the MatLab®