Bayesian flaw characterization from eddy current measurements with grain noise Article Swipe
Jerry A. McMahan
,
John C. Aldrin
,
Eric B. Shell
,
Erin K. Oneida
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1063/1.4974692
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1063/1.4974692
The Bayesian approach to inference from measurement data has the potential to provide highly reliable characterizations of flaw geometry by quantifying the confidence in the estimate results. The accuracy of these confidence estimates depends on the accuracy of the model for the measurement error. Eddy current measurements of electrically anisotropic metals, such as titanium, exhibit a phenomenon called grain noise in which the measurement error is spatially correlated even with no flaw present. We show that the most commonly used statistical model for the measurement error, which fails to account for this correlation, results in overconfidence in the flaw geometry estimates from eddy current data, thereby reducing the effectiveness of the Bayesian approach. We then describe a method of modeling the grain noise as a Gaussian process (GP) using spectral mixture kernels, a type of non-parametric model for the covariance kernel of a GP This provides a broadly applicable, data-driven way of modeling correlation in measurement error. Our results show that incorporation of this noise model results in a more reliable estimate of the flaw and better agreement with the available validation data.
Related Topics
Concepts
Observational error
Noise (video)
Gaussian process
Covariance
Eddy current
Algorithm
Parametric statistics
Bayesian probability
Bayesian inference
Parametric model
Computer science
Eddy-current testing
Gaussian
Mathematics
Statistics
Artificial intelligence
Physics
Quantum mechanics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/1.4974692
- https://aip.scitation.org/doi/pdf/10.1063/1.4974692
- OA Status
- bronze
- Cited By
- 4
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2561621956
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2561621956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/1.4974692Digital Object Identifier
- Title
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Bayesian flaw characterization from eddy current measurements with grain noiseWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-01-01Full publication date if available
- Authors
-
Jerry A. McMahan, John C. Aldrin, Eric B. Shell, Erin K. OneidaList of authors in order
- Landing page
-
https://doi.org/10.1063/1.4974692Publisher landing page
- PDF URL
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https://aip.scitation.org/doi/pdf/10.1063/1.4974692Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
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https://aip.scitation.org/doi/pdf/10.1063/1.4974692Direct OA link when available
- Concepts
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Observational error, Noise (video), Gaussian process, Covariance, Eddy current, Algorithm, Parametric statistics, Bayesian probability, Bayesian inference, Parametric model, Computer science, Eddy-current testing, Gaussian, Mathematics, Statistics, Artificial intelligence, Physics, Quantum mechanics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2022: 1, 2019: 1, 2018: 1, 2017: 1Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1484638608, https://openalex.org/W2082223610, https://openalex.org/W2073510561, https://openalex.org/W1972683174, https://openalex.org/W2054793273, https://openalex.org/W1990442989, https://openalex.org/W2333945668, https://openalex.org/W4211049957, https://openalex.org/W2125849100, https://openalex.org/W2025909913, https://openalex.org/W1523113578, https://openalex.org/W6600518324, https://openalex.org/W2071544114, https://openalex.org/W2148007822, https://openalex.org/W2951686320 |
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