Signal processing and image restoration techniques for two-dimensional eddy current nondestructive evaluation

dc.contributor.advisor John P. Basart
dc.contributor.author Wang, Bing
dc.contributor.department Electrical and Computer Engineering
dc.date 2018-08-23T13:10:55.000
dc.date.accessioned 2020-06-30T07:19:36Z
dc.date.available 2020-06-30T07:19:36Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 1997
dc.date.issued 1997
dc.description.abstract <p>This dissertation presents a comprehensive study on the forward modeling methods, signal processing techniques, and image restoration techniques for two-dimensional eddy current nondestructive evaluation. The basic physical forward method adopted in this study is the volume integral method. We have applied this model to the eddy current modeling problem for half space geometry and thin plate geometry. To reduce the computational complexity of the volume integral method, we have developed a wavelet expansion method which utilizes the multiresolution compression capability of the wavelet basis to greatly reduce the amount of computation with small loss in accuracy. To further improve the speed of forward modeling, we have developed a fast eddy current model based on a radial basis function neural network. This dissertation also contains investigations on signal processing techniques to enhance flaw signals in two-dimensional eddy current inspection data. The processing procedures developed in this study include a set of preprocessing techniques, a background removal technique based on principal component analysis, and grayscale morphological operations to detect flaw signals. Another important part of the dissertation concerns image restoration techniques which can remove the blurring in impedance change images due to the diffusive nature of the eddy current testing. We have developed two approximate linear image restoration methods--the Wiener filtering method and the maximum entropy method. Both linear restoration methods are based on an approximate linear forward model formulated by using the Born approximation. To improve the quality of restoration, we have also developed nonlinear image restoration methods based on simulated annealing and a genetic algorithm. Those nonlinear methods are based on the neural network forward model which is more accurate than the approximate linear forward model.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/12264/
dc.identifier.articleid 13263
dc.identifier.contextkey 6767246
dc.identifier.doi https://doi.org/10.31274/rtd-180813-13537
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/12264
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/65613
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/12264/r_9737777.pdf|||Fri Jan 14 19:16:53 UTC 2022
dc.subject.disciplines Electrical and Electronics
dc.subject.disciplines Electromagnetics and Photonics
dc.subject.disciplines Physics
dc.subject.keywords Electrical and computer engineering
dc.subject.keywords Electrical engineering (Communications and signal processing)
dc.subject.keywords Communications and signal processing
dc.title Signal processing and image restoration techniques for two-dimensional eddy current nondestructive evaluation
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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