Detection of broken rotor bar faults in induction motor at low load using neural network
| dc.contributor.author | BESSAM Besma | |
| dc.date.accessioned | 2024-02-13T16:18:59Z | |
| dc.date.available | 2024-02-13T16:18:59Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | The knowledgeofthebrokenrotorbarscharacteristicfrequenciesandamplitudeshasagreatimportance for allrelateddiagnosticmethods.Themonitoringofmotorfaultsrequiresahighresolutionspectrumto separatedifferentfrequencycomponents.TheDiscreteFourierTransform(DFT)hasbeenwidelyusedto achieve theserequirements.However,atlowslipthistechniquecannotgivegoodresults.Asasolution for theseproblems,this paper proposesanefficient techniquebasedonaneuralnetworkapproachand Hilbert transform(HT)forbrokenrotorbardiagnosisininductionmachinesatlowload.TheHilbert transform isusedtoextractthestatorcurrentenvelope(SCE).Twofeaturesareselectedfromthe(SCE) spectrum (theamplitudeandfrequencyoftheharmonic).Thesefeatureswillbeusedasinputforneural network.Theresultsobtainedareastonishinganditiscapabletodetectthecorrectnumberofbroken rotor bars under different load conditions. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/718 | |
| dc.language.iso | en | |
| dc.title | Detection of broken rotor bar faults in induction motor at low load using neural network | |
| dc.type | Article |