Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data

Basit Öğe Kaydı
dc.authorscopusidİlhami Çolak / 6602990030
dc.authorwosidİlhami Çolak / ABI-4240-2020
dc.contributor.authorCharrak, Naas
dc.contributor.authorDjeddi, Ahmed Zohair
dc.contributor.authorHafaifa, Ahmed
dc.contributor.authorElbar, Mohammed
dc.contributor.authorIratni, Abdelhamid
dc.contributor.authorÇolak, İlhami
dc.date.accessioned2025-04-17T13:33:41Z
dc.date.available2025-04-17T13:33:41Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractControlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
dc.identifier.citationCharrak, N., Djeddi, A. Z., Hafaifa, A., Elbar, M., Iratni, A., & Colak, I. (2024). Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data. Life Cycle Reliability and Safety Engineering, 13(3), 255-275.
dc.identifier.doi10.1007/s41872-024-00259-5
dc.identifier.endpage275
dc.identifier.issn25201352
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85198344999
dc.identifier.scopusqualityQ4
dc.identifier.startpage255
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6273
dc.identifier.volume13
dc.indekslendigikaynakScopus
dc.institutionauthorÇolak, İlhami
dc.institutionauthoridİlhami Çolak / 0000-0002-6405-5938
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofLife Cycle Reliability and Safety Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Fitting
dc.subjectDependability
dc.subjectGas Türbine
dc.subjectGray Wolf Optimizer
dc.subjectJohnson Distributions
dc.subjectReliability Data
dc.subjectSU Distribution
dc.subjectWeibull Distribution
dc.titleJohnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Johnsons-SU-distribution-using-Gray-Wolf-Optimizer-algorithm-for-fitting-gas-turbine-reliability-dataLife-Cycle-Reliability-and-Safety-Engineering.pdf
Boyut:
2.27 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Elektrik-Elektronik Mühendisliği Bölümü Makale Koleksiyonu