Campus Units

Electrical and Computer Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

1-2007

Journal or Book Title

IEEE Transactions on Signal Processing

Volume

55

Issue

1

First Page

372

Last Page

378

DOI

10.1109/TSP.2006.882064

Abstract

We develop a hierarchical Bayesian approach for estimating defect signals from noisy measurements and apply it to nondestructive evaluation (NDE) of materials. We propose a parametric model for the shape of the defect region and assume that the defect signals within this region are 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 then utilized to identify potential defect regions and estimate their size and reflectivity parameters. Our approach provides Bayesian confidence regions (credible sets) for the estimated parameters, which are important in NDE applications. 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. We also outline a simple classification scheme for separating defects from nondefects using estimated mean signals and areas of the potential defects

Comments

This is a manuscript of an article from IEEE Transactions on Signal Processing 55 (2007): 372, doi:10.1109/TSP.2006.882064. Posted with permission.

Rights

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

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