Abstract
First, we present the implementation of a random walk Metropolis-within-Gibbs (MWG) sampling method in flaw characterization based on a metamodeling method. The role of metamodeling is to reduce the computational time cost in Eddy Current Testing (ECT) forward model calculation. In such a way, the use of Markov Chain Monte Carlo (MCMC) methods becomes possible. Secondly, we analyze the influence of partially known parameters in Bayesian estimation. The objective is to evaluate the importance of providing more specific prior information. Simulation results show that even partially known information has great interest in providing more accurate flaw parameter estimations. The improvement ratio depends on the parameter dependence and the interest shows only when the provided information is specific enough.
Highlights
In Eddy Current Testing (ECT), the objective is to estimate the parameters of the flaws present in the specimen examined, like their positions, dimensions and parameters related to their shapes, etc
This, in the inverse problem, limits the use of more sophisticated stochastic methods, like Maximum Likelihood (ML), Maximum A Posteriori (MAP) and Expected A Posteriori (EAP), since these methods are often solved by iterative numerical algorithms [4, 5] or Markov Chain Monte Carlo (MCMC) sampling methods [6, 7, 8]
This work analyzes the influence of partially known parameters on estimation of other unknown parameters
Summary
In Eddy Current Testing (ECT), the objective is to estimate the parameters of the flaws present in the specimen examined, like their positions, dimensions and parameters related to their shapes, etc. This, in the inverse problem, limits the use of more sophisticated stochastic methods, like Maximum Likelihood (ML), Maximum A Posteriori (MAP) and Expected A Posteriori (EAP), since these methods are often solved by iterative numerical algorithms [4, 5] or Markov Chain Monte Carlo (MCMC) sampling methods [6, 7, 8] Those require to calculate f (x) many times. When a forward calculation is required, it only needs to perform the kriging interpolation, which is much less costly computationally For this reason, it makes the use of MCMC methods possible in ECT flaw characterization problem. This is managed by the means of the MCMC sampling method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
