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dc.contributor.authorBegović, Miroslav
dc.contributor.authorDjuric, Petar
dc.contributor.authorPerkel, Joshua
dc.contributor.authorVidakovic, Brani
dc.contributor.authorNovosel, Damir
dc.date.accessioned2008-11-26T14:59:46Z
dc.date.available2008-11-26T14:59:46Z
dc.date.issued2006
dc.identifier.citationMiroslav Begovic, Petar Djuric, Joshua Perkel, Branislav Vidakovic, Damir Novosel, "New Probabilistic Method for Estimation of Equipment Failures and Development of Replacement Strategies," hicss,pp.246a, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06) Track 10, 2006
dc.identifier.urihttp://hdl.handle.net/1853/25836
dc.descriptionPresented at the 39th Hawaii International Conference on System Sciences, 2006en
dc.description©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
dc.description.abstractWhen large amount of statistical information about power system component failure rate is available, statistical parametric models can be developed for predictive maintenance. Often times, only partial information is available: installation date and amount, as well as failure and replacement rates. By combining sufficiently large number of yearly populations of the components, estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system’s short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesBiomedical Engineering Technical Report ; 04/2006en
dc.subjectParametric modelingen
dc.subjectPredictive maintenanceen
dc.subjectPower system componentsen
dc.subjectMonte Carlo simulationsen
dc.titleNew Probabilistic Method for Estimation of Equipment Failures and Development of Replacement Strategiesen
dc.typeTechnical Reporten
dc.contributor.corporatenameGeorgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatenameEmory University. Dept. of Biomedical Engineering
dc.contributor.corporatenameStony Brook University
dc.contributor.corporatenameKEMA T&D Consulting
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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