Reliability investigation of exponentiated Weibull distribution using IPL through numerical and artificial neural network modeling

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dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.date.accessioned2024-11-07T13:32:28Z
dc.date.available2024-11-07T13:32:28Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn current investigation, a novel implementation of intelligent numerical computing solver depending on artificial neural networks (ANN) has been provided to interpret failure function (FF), reliability function (RF), hazard rate function (HRF), Mils ratio (MR), and mean time to failure (MTTF). This study investigates a reliability model centered on the exponentiated Weibull distribution (EWD) and the inverse power law (IPL) model employing the ANN model. The nonmonotonic failure rate can be modeled via this distribution. A data set for the proposed ANN has been generated for various scenarios of (Exponentiated Weibull Inverse Power Law Distribution) EWIPLD model by variation of involved pertinent parameters via the Galerkin weighted residual method (GWRM). Levenberg-Marquard training algorithm has been used in the multi-layer perceptron (MLP) network model developed with 10 nodes in the hidden layer. The Coefficient of Determination (R) value for the ANN model has been obtained as 0.9999. The findings obtained, revealed that ANNs are an excellent technique that can be applied to predict reliability measures in conjunction with the right statistical model.
dc.identifier.doi10.1002/qre.3155
dc.identifier.endpage3631
dc.identifier.issn0748-8017
dc.identifier.issn1099-1638
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85132071388
dc.identifier.scopusqualityQ2
dc.identifier.startpage3616
dc.identifier.urihttps://doi.org/10.1002/qre.3155
dc.identifier.urihttps://hdl.handle.net/11480/15439
dc.identifier.volume38
dc.identifier.wosWOS:000812591900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofQuality and Reliability Engineering International
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectEWIPLD
dc.subjectartificial neural network
dc.subjectpower devices
dc.subjectfeed-forward back-propagation
dc.subjectmean time to failure
dc.titleReliability investigation of exponentiated Weibull distribution using IPL through numerical and artificial neural network modeling
dc.typeArticle

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