This paper present a statistical model parameter identification using Bayesian inference when parameters are correlated and observed data have noise and bias. The method is explained using the Paris model that describes crack growth in a plate under mode I loading. It is assumed the observed data are obtained through structural health monitoring systems, which may have random noise and deterministic bias. It was found that strong correlation exists (a) between two model parameters of the Paris model, and (b) between initially measured crack size and bias. As the level of noise increases, the Bayesian inference was not able to identify the correlated parameters. However, the remaining useful life was predicted accurately because the identification errors in correlated parameters were compensated by each other.
How to Cite
parameters identification, damage growth parameters, correlated parameters, Bayesian inference, structural health monitoring (SHM), remaining useful life (RUL)
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