NDLI: Torque based selection of ANN for fault diagnosis of wound rotor asynchronous motor-converter association

Content Provider	IEEE Xplore Digital Library
Author	Khodja, D.E. Chetate, B.
Copyright Year	2010
Description	Author affiliation: Faculty of Sciences and Engineering Sciences, University Muhamed Boudiaf of M'sila, B.P No 116 Ichebilia (28000), Algeria (Khodja, D.E.) \|\| Research laboratory on the Electrification of industrial enterprises, University of Boumerdès, Avenue de l'indépendence, (35000), Algeria (Chetate, B.)
Abstract	In this paper, an automatic system of diagnosis was developed to detect and locate in real time the defects of the wound rotor asynchronous machine associated to electronic converter. For this purpose, we have treated the signals of the measured parameters (current and speed) to use them firstly, as indicating variables of the machine defects under study and, secondly, as inputs to the Artificial Neuron Network (ANN) for their classification in order to detect the defect type in progress. Once a defect is detected, the interpretation system of information will give the type of the defect and its place of appearance.
Starting Page	339
Ending Page	343
File Size	959523
Page Count	5
File Format	PDF
ISBN	9781424495887
e-ISBN	9786050100136
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-02
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Chambers of Turkish Elec E
Subject Keyword	Motor-converter unit Torque Neurons Rotors Artificial neural networks Converters Master drive Artificial Neuron Networks (ANN) Fault diagnosis Stators Indicating values Effective Value (RMS) Detection Failure Experimental results
Content Type	Text
Resource Type	Article

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