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Uncertainty estimation and quantification applied to fault models of rotating machines

Grant number: 16/13223-6
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): August 01, 2016
Effective date (End): March 31, 2020
Field of knowledge:Engineering - Mechanical Engineering - Mechanics of Solids
Principal researcher:Helio Fiori de Castro
Grantee:Gabriel Yuji Garoli
Home Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:15/20363-6 - Fault tolerant identification and control of rotating systems, AP.TEM
Associated scholarship(s):18/02976-9 - Identification and quantification of uncertainties in a rotor machine using Bayesian inference with generalized polynomial chaos expansion, BE.EP.DD

Abstract

The usage of rotating machines in the industry is vastly. The knowledge of its demeanor is important and the faults that this equipment can suffer as well. The parameters of these faults should be determined, so a better model of the rotor system can be done and the proper maintenance can be made. The parameter has inherent uncertainties and the model of some considered is unknown. Therefore, the principle of maximum entropy and the bayesian inference can be used, combined or separately, to determine these distributions, so they can be propagated to the rotor system dynamic response. Moreover, the accuracy analysis is also considered through an acceptable variation of the system response, and the bayesian inference application is used as an inverse problem method. The stochastic rotor system dynamic response can be estimated by stochastic Galerkin method or stochastic collocation method; both determine projections of the differential equation system in a polynomial space. The uncertainties are expanded in polynomial chaos and, then, the methods mentioned before are applied to solve the stochastic differential equations. (AU)

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GAROLI, GABRIEL Y.; PILOTTO, RAFAEL; NORDMANN, RAINER; DE CASTRO, HELIO F.. Identification of active magnetic bearing parameters in a rotor machine using Bayesian inference with generalized polynomial chaos expansion. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 43, n. 12, . (15/20363-6, 18/02976-9, 16/13223-6)
GAROLI, GABRIEL YUJI; ALVES, DIOGO STUANI; MACHADO, TIAGO HENRIQUE; CAVALCA, KATIA LUCCHESI; DE CASTRO, HELIO FIORI. Fault parameter identification in rotating system: Comparison between deterministic and stochastic approaches. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, v. 20, n. 6, . (16/13223-6, 15/20363-6, 18/21581-5, 18/24600-0)
GAROLI, GABRIEL YUJI; DE CASTRO, HELIO FIORI. Generalized polynomial chaos expansion applied to uncertainties quantification in rotating machinery fault analysis. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, n. 11, . (15/20363-6, 16/13223-6)
GAROLI, GABRIEL YUJI; DE CASTRO, HELIO FIORI. Analysis of a rotor-bearing nonlinear system model considering fluid-induced instability and uncertainties in bearings. Journal of Sound and Vibration, v. 448, p. 108-129, . (16/13223-6, 15/20363-6)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
GAROLI, Gabriel Yuji. Uncertanty quantification and estimation applied in faults models of rotor machines. 2020. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica Campinas, SP.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.