Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11577
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dc.contributor.authorKosova, Furkan-
dc.contributor.authorAltay, Özkan-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2024-06-19T14:55:31Z-
dc.date.available2024-06-19T14:55:31Z-
dc.date.issued2024-
dc.identifier.issn1058-9759-
dc.identifier.issn1477-2671-
dc.identifier.urihttps://doi.org/10.1080/10589759.2024.2350575-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11577-
dc.description.abstractAircraft structures are exposed to a variety of operational and environmental loads that can cause structural deformation and fractures. Structural Health Monitoring (SHM) has emerged as a promising solution for in-situ monitoring of structural components. This article presents a state-of-the-art review of SHM in aviation, current regulations, data acquisition sensors and equipment, and damage detection and identification methods. The article discusses in detail the regulations SHM specific to both civil and military aviation. A comprehensive review of conventional electrical resistance sensors, fiber optic, piezoelectric sensors and smart materials used for SHM monitoring in aircraft structures is then presented. The pros and cons of each data acquisition approach were discussed individually. The damage detection and identification section begins by describing the traditional knowledge-based methods that are combined with expert knowledge and theory, then focuses on the applicability in aircraft SHM systems of spectral or frequency domain models. The last part investigates the new paradigm, machine learning and deep learning methods such as CNN and LSTM on different types of aircraft structures through the existing literature. Furthermore, it covers an emerging approach called physics-informed neural networks (PINN), which combines physics and machine learning, and explore its potential for SHM applications.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) [123M891]en_US
dc.description.sponsorshipThis study is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) through project grant no. [123M891].en_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofNondestructive testing and evaluationen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAircraft structuresen_US
dc.subjectfiber optic sensorsen_US
dc.subjectpiezoelectric sensorsen_US
dc.subjectstructural health monitoringen_US
dc.subjectphysics informed neural networksen_US
dc.subjectShape-memory alloyen_US
dc.subjectprincipal component analysisen_US
dc.subjectfiber-optic sensorsen_US
dc.subjectgaussian mixture modelen_US
dc.subjectneural-networken_US
dc.subjectlife predictionen_US
dc.subjectacoustic-emissionen_US
dc.subjectcomposite structuresen_US
dc.subjectaircraft structuresen_US
dc.subjectsandwich structuresen_US
dc.titleStructural Health Monitoring in Aviation: a Comprehensive Review and Future Directions for Machine Learningen_US
dc.typeReviewen_US
dc.departmentTOBB ETÜen_US
dc.identifier.wosWOS:001229091100001en_US
dc.identifier.scopus2-s2.0-85193933229en_US
dc.institutionauthorKosova, Furkan-
dc.institutionauthorÜnver, Hakkı Özgür-
dc.identifier.doi10.1080/10589759.2024.2350575-
dc.authorscopusid57955774000-
dc.authorscopusid59138739100-
dc.authorscopusid6603873269-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairetypeReview-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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