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dc.contributor.authorVillani, Luis G.G. [UNESP]
dc.contributor.authorda Silva, Samuel [UNESP]
dc.contributor.authorCunha, Americo
dc.date.accessioned2018-12-11T17:22:10Z
dc.date.available2018-12-11T17:22:10Z
dc.date.issued2018-01-01
dc.identifierhttp://dx.doi.org/10.1016/j.ymssp.2018.07.028
dc.identifier.citationMechanical Systems and Signal Processing.
dc.identifier.issn1096-1216
dc.identifier.issn0888-3270
dc.identifier.urihttp://hdl.handle.net/11449/176714
dc.description.abstractThe damage detection problem in mechanical systems, using vibration measurements, is commonly called Structural Health Monitoring (SHM). Many tools are able to detect damages by changes in the vibration pattern, mainly, when damages induce nonlinear behavior. However, a more difficult problem is to detect structural variation associated with damage, when the mechanical system has nonlinear behavior even in the reference condition. In these cases, more sophisticated methods are required to detect if the changes in the response are based on some structural variation or changes in the vibration regime, because both can generate nonlinearities. Among the many ways to solve this problem, the use of the Volterra series has several favorable points, because they are a generalization of the linear convolution, allowing the separation of linear and nonlinear contributions by input filtering through the Volterra kernels. On the other hand, the presence of uncertainties in mechanical systems, due to noise, geometric imperfections, manufacturing irregularities, environmental conditions, and others, can also change the responses, becoming more difficult the damage detection procedure. An approach based on a stochastic version of Volterra series is proposed to be used in the detection of a breathing crack in a beam vibrating in a nonlinear regime of motion, even in reference condition (without crack). The system uncertainties are simulated by the variation imposed in the linear stiffness and damping coefficient. The results show, that the nonlinear analysis done, considering the high order Volterra kernels, allows the approach to detect the crack with a small propagation and probability confidence, even in the presence of uncertainties.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)
dc.language.isoeng
dc.relation.ispartofMechanical Systems and Signal Processing
dc.sourceScopus
dc.subjectDamage detection
dc.subjectNonlinear dynamics
dc.subjectStochastic Volterra series
dc.subjectUncertainties quantification
dc.titleDamage detection in uncertain nonlinear systems based on stochastic Volterra seriesen
dc.typeArtigo
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade do Estado do Rio de Janeiro (UERJ)
dc.description.affiliationUNESP – Universidade Estadual Paulista Faculdade de Engenharia de Ilha Solteira Departamento de Engenharia Mecânica, Av. Brasil, 56
dc.description.affiliationUERJ – Universidade do Estado do Rio de Janeiro NUMERICO – Nucleus of Modeling and Experimentation with Computers, R. São Francisco Xavier, 524
dc.description.affiliationUnespUNESP – Universidade Estadual Paulista Faculdade de Engenharia de Ilha Solteira Departamento de Engenharia Mecânica, Av. Brasil, 56
dc.identifier.doi10.1016/j.ymssp.2018.07.028
dc.rights.accessRightsAcesso aberto
dc.description.sponsorshipIdFAPESP: 2012/09135-3
dc.description.sponsorshipIdFAPESP: 2015/25676-2
dc.description.sponsorshipIdCNPq: 307520/2016-1
dc.description.sponsorshipIdFAPERJ: E-26/010.002178/2015
dc.identifier.scopus2-s2.0-85051508961
dc.identifier.file2-s2.0-85051508961.pdf
dc.relation.ispartofsjr1,805
dc.relation.ispartofsjr1,805
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