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

Author:

Ye Bo1ORCID,Shu Hongchun1,Cao Min2,Zeng Fang1,Qiu Gefei1,Dong Jun1,Zhang Wenying1,Shan Jieshan1

Affiliation:

1. Engineering Research Center of Smart Grid, Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

2. Oxbridge College, Kunming University of Science and Technology, Kunming 650106, China

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Pulse-modulation Eddy Current Imaging and Evaluation of Hidden Flaws in Layered Conductors;2021 IEEE Far East NDT New Technology & Application Forum (FENDT);2021-12-14

2. Bayesian dynamic forecasting of structural strain response using structural health monitoring data;Structural Control and Health Monitoring;2020-06-09

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