Application of statistical neuronal networks for diagnostics of induction machine rotor faults

Induction machines are extensively used in industries and are subject to unexpected breakdowns. It is necessary, therefore, to prevent them from such breakdown through the maintenance that works according to a well-trained planning. A considerable number of diagnosis techniques have been used such a...

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Bibliographic Details
Published inSTA : proceedings : 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering : December 19-21, 2016, Sousse, Tunisia pp. 199 - 204
Main Authors Marmouch, Sameh, Aroui, Tarek, Koubaa, Yassine
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
analysis motor current signature analysis
Bars
Biological neural networks
induction machines
Induction motors
Neurons
probabilistic neuronal network
Radial basis function networks
radial basis functions neuronal network
Rotors
Training
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Summary:Induction machines are extensively used in industries and are subject to unexpected breakdowns. It is necessary, therefore, to prevent them from such breakdown through the maintenance that works according to a well-trained planning. A considerable number of diagnosis techniques have been used such as Motor Current Signature Analysis (MCSA), Axial Flux Monitoring and Vibration Monitoring. This paper shows the effectiveness of the artificial neuronal network (radial basis function neuronal network and the probabilistic neuronal network) basis on MCSA for rotor faults diagnosis.
DOI:10.1109/STA.2016.7952063