An effective method for detection of stator fault in PMSM with 1D-LBP

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dc.contributor.authorMinaz, Mehmet Recep
dc.date.accessioned2024-12-24T19:27:20Z
dc.date.available2024-12-24T19:27:20Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractPermanent Magnet Synchronous Motors (PMSMs) have recently been used commonly in all areas of the industry due to their position control as well as precise speed. The success of these motors in applications of precise speed and position control depends on their whole operation. Even if the fault is at a highly-low-level, this negatively affects the precision of the motor. In this study, the one dimensional local binary patterns (1D-LBP) method, which is compelling and distinctive, has been used for feature extraction instead of frequency spectrum analysis or time-frequency analysis, which are among conventional feature extraction techniques in the literature, to detect short-circuit fault that occurs in PMSM stators. Thus, to test the proposed method, an experiment setup has been prepared to record current and voltage signals detected through 15 kHz sampling from healthy and faulty PMSM. 1D-LBP was applied to these current and voltage signals and the histograms of newly formed current and voltage signals were obtained. Histograms of newly formed signals are used as feature vectors. Healthy and faulty motors could be classified at high success rates applying one of the machine learning techniques, Knn algorithm, to histograms. It was found that the methods had a success rate over 90% when it was tested over-current and voltage data obtained from PMSM that ran at different speeds and loads and had different fault rates to test whether the methods ran properly. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
dc.description.sponsorshipSiirt University Special Electrical laboratory
dc.description.sponsorshipThe author would like to thank Siirt University Special Electrical laboratory for supporting this research presented in this paper.
dc.identifier.doi10.1016/j.isatra.2020.07.013
dc.identifier.endpage292
dc.identifier.issn0019-0578
dc.identifier.issn1879-2022
dc.identifier.pmid32682547
dc.identifier.scopus2-s2.0-85087991695
dc.identifier.scopusqualityQ1
dc.identifier.startpage283
dc.identifier.urihttps://doi.org/10.1016/j.isatra.2020.07.013
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6607
dc.identifier.volume106
dc.identifier.wosWOS:000598662200004
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Science Inc
dc.relation.ispartofIsa Transactions
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectFeature extraction
dc.subject1D-LBP
dc.subjectFault detection
dc.subjectStator fault
dc.subjectPMSM
dc.titleAn effective method for detection of stator fault in PMSM with 1D-LBP
dc.typeArticle

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