Uncertainty Identification of Damage Growth Parameters using Health Monitoring Data and Nonlinear Regression
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Abstract
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.
How to Cite
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remaining useful life (RUL), structural health monitoring, Uncertainty Quantification, prognosis, non-linear least square, damage propagation
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