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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn978-3-319-99271-6; 978-3-319-99272-3pt_BR
dc.contributor.authorunicampGaroli, Gabriel Yuji-
dc.contributor.authorunicampTyminski, Natalia Cezaro-
dc.contributor.authorunicampCastro, Hélio Fiori de-
dc.typeOutro documentopt_BR
dc.titleStochastic collocation approach to bayesian inference applied to rotating system parameter identificationpt_BR
dc.contributor.authorGaroli, G.Y.-
dc.contributor.authorTyminski, N.C.-
dc.contributor.authorde Castro, H.F.-
dc.subjectInferência bayesianapt_BR
dc.subject.otherlanguageBayesian inferencept_BR
dc.description.abstractThe analyzed problem is the identification of fault parameters taking into account the stochastic characteristic of the system. The objective is to estimate the unbalance parameters, as the unbalance moment, phase angle and axial position of the unbalance force applied to the rotor. Therefore, experimental tests with the rotor to obtain the unbalance response is performed. This work aims the comparison between Bayesian inference with Markov Chain Monte Carlo method (MCMC), using Delayed Rejection Adaptive Metropolis algorithm (DRAM), and Stochastic Collocation through Generalized polynomial chaos expansion. This method has computational cost smaller than the MCMC methods, and it could be used as an alternative method for stochastic simulation. The Bayesian inference with MCMC and DRAM is based on previous works. However, the application of the MCMC have a high computational cost. Therefore, the Stochastic collocation is introduced into the likelihood function of the Bayes theorem for a faster convergence rate. The low computational cost of the collocation is evaluated and the results of both methods are compared to determine the convergence and precision of the collocation method.pt_BR
dc.relation.ispartofMechanisms and machine sciencept_BR
dc.publisher.cityDordrechtpt_BR
dc.publisher.countryHolandapt_BR
dc.publisherSpringerpt_BR
dc.date.issued2018-
dc.date.monthofcirculationAug.pt_BR
dc.language.isoengpt_BR
dc.description.volume63pt_BR
dc.description.firstpage401pt_BR
dc.description.lastpage415pt_BR
dc.rightsFechadopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn2211-0984pt_BR
dc.identifier.eissn2211-0992pt_BR
dc.identifier.doi10.1007/978-3-319-99272-3_28pt_BR
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-319-99272-3_28pt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumberSem informaçãopt_BR
dc.description.sponsordocumentnumber2015/ 20363-6; 2016/13059-1pt_BR
dc.date.available2020-05-21T12:22:02Z-
dc.date.accessioned2020-05-21T12:22:02Z-
dc.description.provenanceSubmitted by Bruna Maria Campos da Cunha (bcampos@unicamp.br) on 2020-05-21T12:22:02Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:17:45Z : No. of bitstreams: 1 2-s2.0-85052213633.pdf: 1205298 bytes, checksum: 1f1c50a99c498819e5a363864f2a2293 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-21T12:22:02Z (GMT). No. of bitstreams: 0 Previous issue date: 2018en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/341865-
dc.description.conferencenome10. International Conference on Rotor Dynamicspt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentDepartamento de Projeto Mecânicopt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.subject.keywordBayesian networkspt_BR
dc.subject.keywordComputation theorypt_BR
dc.subject.keywordInference enginespt_BR
dc.subject.keywordMarkov processespt_BR
dc.subject.keywordMonte Carlo methodspt_BR
dc.subject.keywordParameter estimationpt_BR
dc.subject.keywordStochastic modelspt_BR
dc.subject.keywordBayesian inferencept_BR
dc.subject.keywordGeneralized polynomial chaospt_BR
dc.subject.keywordLikelihood functionspt_BR
dc.subject.keywordMarkov chain Monte Carlo methodpt_BR
dc.subject.keywordRotor-dynamicspt_BR
dc.subject.keywordStochastic characteristicpt_BR
dc.subject.keywordStochastic collocationpt_BR
dc.subject.keywordStochastic simulationspt_BR
dc.subject.keywordStochastic systemspt_BR
dc.identifier.source2-s2.0-85052213633pt_BR
dc.creator.orcidorcid.org/0000-0002-4304-1203pt_BR
dc.creator.orcidSem informaçãopt_BR
dc.creator.orcidSem informaçãopt_BR
dc.type.formCapítulo de livropt_BR
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