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dc.contributor.author |
Benyahia Oussama, Belazzoug Walid |
|
dc.date.accessioned |
2022-11-09T08:05:35Z |
|
dc.date.available |
2022-11-09T08:05:35Z |
|
dc.date.issued |
2022-06-25 |
|
dc.identifier.uri |
https://dspace.univ-bba.dz:443/xmlui/handle/123456789/2288 |
|
dc.description.abstract |
In this work, we have modeled and simulated the asynchronous machine for the
diagnosis of the rotor bar breakage fault. This diagnosis is based on two techniques. The first is
the application of signal processing which is an efficient solution to the problem of fault
diagnosis. The second is based on a neural network, which is one of the best tools for automatic
fault diagnosis. This technique allows to diagnose and determine the number of broken bars
despite the change of the load. |
en_US |
dc.language.iso |
fr |
en_US |
dc.publisher |
faculté des sciences et de la technologie univ bba |
en_US |
dc.relation.ispartofseries |
;EM/M/2022/12 |
|
dc.subject |
Diagnosis, asynchronous machine, signal processing, neural network |
en_US |
dc.title |
Diagnostic d’un défaut rotorique dans les moteurs asynchrones triphasés à cage par RNA |
en_US |
dc.type |
Thesis |
en_US |
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