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Technique de diagnostic des défauts dans les systèmes électriques par les réseaux de neurones (machine asynchrone)

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dc.contributor.author AL-MUSHIAA Mohammed Mansoor ➢ AOUKLI Zakaria
dc.date.accessioned 2022-11-08T12:34:51Z
dc.date.available 2022-11-08T12:34:51Z
dc.date.issued 2022-06
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/2280
dc.description.abstract The asynchronous machine is the most used in industry due to its robustness and low purchase or maintenance cost, but it can be exposed to many electrical or mechanical faults during its operation, which requires early detection. This has led to the use of many diagnostic methods that allow us to identify and classify faults that occur in the machine, among these techniques is the use of artificial intelligence and artificial neural networks. The objective of this work is to diagnose malfunctions of squirrel cage induction machines (broken bar fault) using artificial neural network techniques. We developed a neural network model to detect and classify defects, then we performed tests to validate the neural network model 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/2022/06
dc.subject Asynchronous machine; fault diagnosis; defect of broken bars; artificial neural network. en_US
dc.title Technique de diagnostic des défauts dans les systèmes électriques par les réseaux de neurones (machine asynchrone) en_US
dc.type Thesis en_US


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