a bayesian network method for quantitative evaluation of defects in multilayered structures from eddy current ndt signals

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ID: 178421
2014
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Abstract
Accurate evaluation and characterization of defects in multilayered structures from eddy current nondestructive testing (NDT) signals are a difficult inverse problem. There is scope for improving the current methods used for solving the inverse problem by incorporating information of uncertainty in the inspection process. Here, we propose to evaluate defects quantitatively from eddy current NDT signals using Bayesian networks (BNs). BNs are a useful method in handling uncertainty in the inspection process, eventually leading to the more accurate results. The domain knowledge and the experimental data are used to generate the BN models. The models are applied to predict the signals corresponding to different defect characteristic parameters or to estimate defect characteristic parameters from eddy current signals in real time. Finally, the estimation results are analyzed. Compared to the least squares regression method, BNs are more robust with higher accuracy and have the advantage of being a bidirectional inferential mechanism. This approach allows results to be obtained in the form of full marginal conditional probability distributions, providing more information on the defect. The feasibility of BNs presented and discussed in this paper has been validated.
Reference Key
ye2014mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Bo Ye;Hongchun Shu;Min Cao;Fang Zeng;Gefei Qiu;Jun Dong;Wenying Zhang;Jieshan Shan
Journal journal of power sources
Year 2014
DOI
10.1155/2014/405707
URL
http://dx.doi.org/10.1155/2014/405707
Keywords
civil engineering (general)mathematics engineering (general)

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