Prognostics and Health Management of Wind Energy Infrastructure Systems

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dc.authorid0000-0002-2740-8183
dc.authorid0000-0002-2422-5752
dc.authorid0000-0003-4203-8237
dc.authorid0000-0002-6932-1481
dc.authorid0000-0001-8474-7328
dc.authorid0000-0002-9548-8648
dc.contributor.authorYuce, Celalettin
dc.contributor.authorGecgel, Ozhan
dc.contributor.authorDogan, Oguz
dc.contributor.authorDabetwar, Shweta
dc.contributor.authorYanik, Yasar
dc.contributor.authorKalay, Onur Can
dc.contributor.authorEkwaro-Osire, Stephen
dc.date.accessioned2026-02-12T21:05:28Z
dc.date.available2026-02-12T21:05:28Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy Infrastructure. To address these aspects, four research questions were formulated. What s the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis. A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.
dc.identifier.doi10.1115/1.4053422
dc.identifier.issn2332-9017
dc.identifier.issn2332-9025
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85134034061
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1115/1.4053422
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6957
dc.identifier.volume8
dc.identifier.wosWOS:000790396500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAsme
dc.relation.ispartofAsce-Asme Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectIntelligent Fault-Diagnosis
dc.subjectConvolutional Neural-Network
dc.subjectDigital Twin Feasibility
dc.subjectData Augmentation
dc.subjectPower Curve
dc.subjectNondeterministic Predictions
dc.subjectUncertainty Quantification
dc.subjectDesign Framework
dc.subjectDynamic-Response
dc.subjectFatigue Life
dc.titlePrognostics and Health Management of Wind Energy Infrastructure Systems
dc.typeArticle

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