dc.contributorTomazella, Vera Lucia Damasceno
dc.contributorhttp://lattes.cnpq.br/8870556978317000
dc.contributorhttp://lattes.cnpq.br/8952048121396398
dc.creatorMorita, Lia Hanna Martins
dc.date.accessioned2017-09-25T18:27:54Z
dc.date.available2017-09-25T18:27:54Z
dc.date.created2017-09-25T18:27:54Z
dc.date.issued2017-04-07
dc.identifierMORITA, Lia Hanna Martins. Degradation modeling for reliability analysis with time-dependent structure based on the inverse gaussian distribution. 2017. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9120.
dc.identifierhttps://repositorio.ufscar.br/handle/ufscar/9120
dc.description.abstractConventional reliability analysis techniques are focused on the occurrence of failures over time. However, in certain situations where the occurrence of failures is tiny or almost null, the estimation of the quantities that describe the failure process is compromised. In this context the degradation models were developed, which have as experimental data not the failure, but some quality characteristic attached to it. Degradation analysis can provide information about the components lifetime distribution without actually observing failures. In this thesis we proposed different methodologies for degradation data based on the inverse Gaussian distribution. Initially, we introduced the inverse Gaussian deterioration rate model for degradation data and a study of its asymptotic properties with simulated data. We then proposed an inverse Gaussian process model with frailty as a feasible tool to explore the influence of unobserved covariates, and a comparative study with the traditional inverse Gaussian process based on simulated data was made. We also presented a mixture inverse Gaussian process model in burn-in tests, whose main interest is to determine the burn-in time and the optimal cutoff point that screen out the weak units from the normal ones in a production row, and a misspecification study was carried out with the Wiener and gamma processes. Finally, we considered a more flexible model with a set of cutoff points, wherein the misclassification probabilities are obtained by the exact method with the bivariate inverse Gaussian distribution or an approximate method based on copula theory. The application of the methodology was based on three real datasets in the literature: the degradation of LASER components, locomotive wheels and cracks in metals.
dc.languageeng
dc.publisherUniversidade Federal de São Carlos
dc.publisherUFSCar
dc.publisherPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEs
dc.publisherCâmpus São Carlos
dc.rightsAcesso aberto
dc.subjectAnálise de degradação
dc.subjectDistribuição gaussiana inversa
dc.subjectProcesso gaussiano inverso
dc.subjectFragilidade
dc.subjectTestes de burn-in
dc.subjectDegradation analysis
dc.subjectInverse gaussian distribution
dc.subjectInverse gaussian process
dc.subjectFrailty
dc.subjectBurn-in tests
dc.titleDegradation modeling for reliability analysis with time-dependent structure based on the inverse gaussian distribution
dc.typeTesis


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