Uncertainty Identification of Damage Growth Parameters using Health Monitoring Data and Nonlinear Regression
When it comes to identifying model parameters such as damage growth parameters in Paris law for example, Bayesian inference is a popular method. However, it involves substantial computational cost, especially with increasing number of parameters. When the prior distribution for the parameters is not narrow, non-linear regression may provide almost all the benefits of Bayesian updating at a small fraction of the computational cost. In this paper we apply this approach to the identification of damage growth parameters. As a first step we simplify the problem to a single parameter in order to compare it with the same problem solved using Bayesian inference. We then discuss the issues related to uncertainty quantification in the case of a highly non-linear problem.
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remaining useful life (RUL), structural health monitoring, Uncertainty Quantification, prognosis, non-linear least square, damage propagation
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