Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Determining the optimal maintenance action for a deteriorating repairable system

Full metadata record
DC Field Value Language
dc.contributor.authorKim, Teak-Soo-
dc.contributor.authorPark, Chong-Sung-
dc.contributor.authorAhn, Suneung-
dc.date.accessioned2021-06-23T18:04:15Z-
dc.date.available2021-06-23T18:04:15Z-
dc.date.issued2008-01-
dc.identifier.issn0266-8920-
dc.identifier.issn1878-4275-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/42708-
dc.description.abstractThis paper develops a decision model for risk management of the deterioration of a repairable system. When a failure occurs in a deteriorating system, an optimal maintenance decision that includes the possibility of system replacement, as compared to mere deterioration reduction, should be made. There are many uncertainties associated with deterioration, however, so the decision may require a probabilistic analysis. Here, a well-known nonhomogeneous Poisson process with a power law intensity function is used to model the uncertain behavior of the deteriorating system. A Bayesian statistical approach is adopted to allow for the uncertainty of the parameters of the power law intensity function, which imposes a conjugate prior distribution of the parameters. A power law maintenance cost function and the failure cost are analyzed to determine the magnitude of failure risk reduction by minimizing the expected cost incurred from the maintenance action and future failures. A numerical example is given. (c) 2007 Elsevier Ltd. All rights reserved.-
dc.format.extent7-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier BV-
dc.titleDetermining the optimal maintenance action for a deteriorating repairable system-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1016/j.probengmech.2007.12.003-
dc.identifier.scopusid2-s2.0-38349050025-
dc.identifier.wosid000253747400010-
dc.identifier.bibliographicCitationProbabilistic Engineering Mechanics, v.23, no.1, pp 95 - 101-
dc.citation.titleProbabilistic Engineering Mechanics-
dc.citation.volume23-
dc.citation.number1-
dc.citation.startPage95-
dc.citation.endPage101-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMechanics-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryEngineering, Mechanical-
dc.relation.journalWebOfScienceCategoryMechanics-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusRELIABILITY-
dc.subject.keywordAuthorpower law failure model-
dc.subject.keywordAuthorBayesian probability model-
dc.subject.keywordAuthorprior distribution-
dc.subject.keywordAuthormaintenance cost-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0266892007000550?pes=vor-
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF INDUSTRIAL & MANAGEMENT ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE