Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

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dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.authoridLone, Showkat Ahmad/0000-0001-7149-3314
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridSwarup, Chetan/0000-0002-8637-3945
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorSwarup, Chetan
dc.contributor.authorSindhu, Tabassum Naz
dc.contributor.authorLone, Showkat Ahmad
dc.date.accessioned2024-11-07T13:34:16Z
dc.date.available2024-11-07T13:34:16Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi-layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.
dc.identifier.doi10.1002/adts.202200100
dc.identifier.issn2513-0390
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85131066721
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/adts.202200100
dc.identifier.urihttps://hdl.handle.net/11480/15891
dc.identifier.volume5
dc.identifier.wosWOS:000804654800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-V C H Verlag Gmbh
dc.relation.ispartofAdvanced Theory and Simulations
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectartificial neural networks
dc.subjectmixture models
dc.subjectmean inactivity time
dc.subjectmean residual life
dc.subjectmean time to failure
dc.subjectreliability function
dc.titleReliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network
dc.typeArticle

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