A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

dc.contributor.author Holland, Stephen
dc.contributor.author Tian, Ye
dc.contributor.author Maitra, Ranjan
dc.contributor.author Meeker, William
dc.contributor.author Meeker, William
dc.contributor.author Holland, Stephen
dc.contributor.department Aerospace Engineering
dc.contributor.department Statistics
dc.date 2019-09-21T14:51:22.000
dc.date.accessioned 2020-07-02T06:58:04Z
dc.date.available 2020-07-02T06:58:04Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2017-08-18
dc.date.issued 2017-01-01
dc.description.abstract <p>Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants and oil pipelines in order to prevent catastrophic events. Many modern NDE systems generate image data. In some applications an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws.</p>
dc.description.comments <p>This is an Accepted Manuscript of an article published by Taylor & Francis as Tian, Ye, Ranjan Maitra, William Q. Meeker, and Stephen D. Holland. "A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images." <em>Technometrics</em> 59, no. 2 (2017): 247-261. DOI: <a href="http://dx.doi.org/10.1080/00401706.2016.1153000" target="_blank">10.1080/00401706.2016.1153000</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/78/
dc.identifier.articleid 1070
dc.identifier.contextkey 8820084
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/78
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90679
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/78/2016_MaitraR_Statistical_FrameworkImproved.pdf|||Sat Jan 15 01:54:09 UTC 2022
dc.source.uri 10.1080/00401706.2016.1153000
dc.subject.disciplines Aerospace Engineering
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Dynamic thresholding
dc.subject.keywords Image processing
dc.subject.keywords Matched filter
dc.subject.keywords Noise-Interference Model
dc.subject.keywords Probability of Detection
dc.subject.keywords Signal-to-Noise Ratio
dc.title A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images
dc.type article
dc.type.genre article
dspace.entity.type Publication
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