Detection of multiple bolt loosening via data based statistical pattern recognition techniques

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dc.authorscopusid36716504700
dc.contributor.authorPekedis, Mahmut
dc.date.accessioned2023-01-12T20:11:18Z
dc.date.available2023-01-12T20:11:18Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description.abstractThe main objective of this research is to diagnose single or multiple bolt loosening for a system exposed to environmental and operational uncertain conditions by implementing both vector auto regressive (VAR) model alone and VAR model coupled with singular value decomposition (SVD), Mahalanobis distance and principal component analysis (PCA). The research has been deployed on a three-storey system constructed with aluminum members in the laboratory medium. The damage simulation scenarios in system have been performed by loosening the frame bolts on each floor which cause the nonlinear effects. The system's ground storey has been excited with an electromagnetic shaker vibrating at band limited random frequencies. Accelerometers are attached to each edge of the floor to acquire the dynamic response of the structure and use their signals for damage diagnosis. The accelerometers' measurements were collected for eight loosening cases. Once these measurements have been processed and evaluated in statistical pattern recognition algorithms, their performance results have been compared and presented via tables and ROC curves. It is obtained from ROC curves that the VAR model coupled with PCA technique has the highest diagnosis performance score in terms of area under curve (AUC) and optimum true positive rate (TPR). The approach it has been followed here demonstrates that the individual sensor that is most affected by the loosening can be identified which could be implemented to detect the bolt loosening.en_US
dc.identifier.doi10.17341/gazimmfd.820157
dc.identifier.endpage2010en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85117797229en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1993en_US
dc.identifier.trdizinid494874en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.820157
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/494874
dc.identifier.urihttps://hdl.handle.net/11454/78082
dc.identifier.volume36en_US
dc.identifier.wosWOS:000692521900017en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isotren_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal Of The Faculty Of Engineering And Architecture Of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBolt looseningen_US
dc.subjectpattern recognitionen_US
dc.subjectstructural health monitoringen_US
dc.subjectvector-auto regressive modelsen_US
dc.subjectsingular value decompositionen_US
dc.subjectMahalanobis distanceen_US
dc.subjectDamage Diagnosisen_US
dc.subjectModelsen_US
dc.titleDetection of multiple bolt loosening via data based statistical pattern recognition techniquesen_US
dc.title.alternativeBirden çok cıvata gevşemesinin veri tabanlı istatistiki örüntü tanıma teknikleriyle tespitien_US
dc.typeArticleen_US

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