A Bayesian Network Method for Quantitative Evaluation of Defects in Multilayered Structures from Eddy Current NDT Signals

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...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (1)
Main Authors: Ye, Bo, Shu, Hongchun, Cao, Min, Zeng, Fang, Qiu, Gefei, Dong, Jun, Zhang, Wenying, Shan, Jieshan
Format: Article
Language:English
Subjects:
Advantages
Bayesian analysis
Conditional probability
Defects
Eddy current testing
Eddy currents
Electricity distribution
Inspection
Inverse problems
Least squares method
Mathematical analysis
Mathematical models
Mathematical problems
Methods
Networks
Neural networks
Nondestructive testing
Parameter estimation
Probability
Process parameters
Sampling techniques
Science
Signal processing
Statistical analysis
Uncertainty
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/405707