Induction Machine Faults Detection and Localization by Neural Networks Methods

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dc.contributor.authorChouidira, Ibrahim
dc.contributor.authorKhodja, Djalal Eddine
dc.contributor.authorChakroune, Salim
dc.date.accessioned2021-01-10T08:12:12Z
dc.date.available2021-01-10T08:12:12Z
dc.date.issued2019
dc.description.abstractThe 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 short-circuit 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 accuracyen_US
dc.description.sponsorship, and localizationen_US
dc.identifier.issn1958-5748
dc.identifier.otherdoi.org/10.18280/ria.330604
dc.identifier.urihttp://www.iieta.org/journals/ria/paper/10.18280/ria.330604
dc.identifier.urihttps://dspace.univ-boumerdes.dz/handle/123456789/6098
dc.language.isoenen_US
dc.publisherIIETAen_US
dc.relation.ispartofseriesRevue d'Intelligence Artificielle, 33(6);pp. 427-434
dc.subjectInduction machineen_US
dc.subjectFaults detection and localizationen_US
dc.subjectBroken barsen_US
dc.titleInduction Machine Faults Detection and Localization by Neural Networks Methodsen_US
dc.typeArticleen_US

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