Bayesian inference applied to journal bearing parameter identification

Stochastic methods application is emergent in engineering field, leading designers to better solutions during product development. The stochastic characteristic of system parameters, such as geometric dimensions, operating conditions, among others, may lead to unexpected or even undesirable behavior, making it mandatory to take into account the parameters' uncertainties aiming a robust project. An approach considered here to the uncertainties distribution model is the Bayesian inference. This method gives the estimation of the stochastic parameter from previous information and observations of experimental response. After that, it is possible to proceed with the correspondent propagation on the system response. In the context of rotor dynamics, stochastic methods are not yet scattered and deterministic approaches still prevail. This work aims the use of Bayesian inference, particularly the Markov Chain Monte Carlo method, in a simple rotor-bearing system model to evaluate the influence of uncertainties in the journal bearings parameters on the overall behavior of these components. The critical parameters considered here are radial clearance and oil viscosity as function of temperature39829833004CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão temNão temNão temThe authors would like to thank CENPES-PETROBRAS, FAPESP, FAEPEX-UNICAMP, CAPES and CNPq for supporting this researc