Application of Bayesian inference to unbalance identification in rotors

Abstract

This work proposes the use of Bayesian inference to fault parameters identification 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 are performed. The statistical distribution of each parameter is obtained using a Markov Chain Monte Carlo method (MCMC), simulated with the Delayed Rejection Adaptive Metropolis algorithm (DRAM). Thus, the residual between experimental and numerical response is calculated and applied to a Bayesian inference analysis, obtaining information about the unbalance parameters, which are summarized in statistics for each parameter distributions.