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Bayesian Defect Signal Analysis
AIP Conference Proceedings
  • Aleksandar Dogandžić, Iowa State University
  • Benhong Zhang, Iowa State University
Document Type
Conference Proceeding
Publication Date
We develop a Bayesian framework for estimating defect signals from noisy measurements. We propose a parametric model for the shape of the defect region and assume that the defect signal within this region is random with unknown mean and variance. Markov chain Monte Carlo (MCMC) algorithms are derived for simulating from the posterior distributions of the model parameters and defect signals. These algorithms are utilized to identify potential defect regions and estimate their size and reflectivity. We specialize the proposed framework to elliptical defect shape and Gaussian signal and noise models and apply it to experimental ultrasonic C‐scan data from an inspection of a cylindrical titanium billet.

The following article appeared in AIP Conference Proceedings 820 (2006): 820 and may be found at doi:10.1063/1.2184584.

Copyright 2006 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.
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American Institute of Physics
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Citation Information
Aleksandar Dogandžić and Benhong Zhang. "Bayesian Defect Signal Analysis" AIP Conference Proceedings Vol. 820 (2006) p. 617 - 624
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