Electric fault diagnosis and detection in an induction machine using RMS based method

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dc.contributor.authorAkrad, Ahmad
dc.contributor.authorSehab, Rabia
dc.contributor.authorAlyoussef, Fadi
dc.date.accessioned2024-04-24T17:56:25Z
dc.date.available2024-04-24T17:56:25Z
dc.date.issued2022
dc.departmentDicle Üniversitesi, Fen Bilimleri Enstitüsü, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.description2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022 -- 13 July 2022 through 15 July 2022 -- -- 182187en_US
dc.description.abstractNowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a new method is developed for fault detection using residual errors generated by the root mean square (RMS) of phase currents. This approach is based on an asymmetric nonlinear model of Induction Machine where the winding fault of the three axes frame state space is taken into account. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults. © 2022 IEEE.en_US
dc.identifier.citationAkrad, A., Sehab, R. ve Alyoussef, F. (2022). Electric fault diagnosis and detection in an induction machine using RMS based method. 2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022.
dc.identifier.doi10.1109/ICCAD55197.2022.9853878
dc.identifier.isbn9781665497947
dc.identifier.scopus2-s2.0-85137826449
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ICCAD55197.2022.9853878
dc.identifier.urihttps://hdl.handle.net/11468/23502
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAsymmetric Nonlinear Modelen_US
dc.subjectCurrent sensor faulten_US
dc.subjectFault detectionen_US
dc.subjectFault diagnosisen_US
dc.subjectInduction machineen_US
dc.subjectInter-Turn Short-Circuit Faulten_US
dc.subjectIsolationen_US
dc.subjectRoot mean squareen_US
dc.titleElectric fault diagnosis and detection in an induction machine using RMS based methoden_US
dc.titleElectric fault diagnosis and detection in an induction machine using RMS based method
dc.typeConference Objecten_US

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