Statistical Methods for Probability of Detection in Structural Health Monitoring

dc.contributor.author Meeker, William
dc.contributor.author Roach, Dennis
dc.contributor.author Meeker, William
dc.contributor.author Kessler, Seth
dc.contributor.department Statistics
dc.contributor.department Center for Nondestructive Evaluation (CNDE)
dc.date 2019-12-12T20:16:27.000
dc.date.accessioned 2020-07-02T06:57:42Z
dc.date.available 2020-07-02T06:57:42Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-09-29
dc.description.abstract <p>There is much interest in the potential to use Structural Health Monitoring (SHM) technology to augment traditional Nondestructive Evaluation (NDE) methods to improve safety, increase asset availability, and reduce maintenance and inspection costs. SHM has the potential to be used in many areas of application including critical components in aircraft and pipelines. Probability of detection (POD) plays a critical role in aircraft structural integrity programs. As such, there has been a high interest in developing methods that can be used to assess POD in SHM applications. In contrast to traditional NDE laboratory experiments to assess POD that involve a set of specimens with cracks, SHM sensors are fixed and SHM data are acquired over time as cracks grow or otherwise evolve. Traditional statistical methods for assessing POD (e.g., as described in MIL-HDBK 1823A 2009) have to be extended to properly handle repeated-measures data. This purpose of this paper is to review the basic statistical concepts of probability of detection (POD) and to show how these concepts can and should be applied to SHM POD studies by modifying and extending existing methods for estimating POD. The methods presented here are applicable when there is a scalar damage index or other response that will be used to make a detect decision. The paper compares a simple model based on length at detection and a random effects model to describe repeated measures data.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/291/
dc.identifier.articleid 1292
dc.identifier.contextkey 15975355
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/291
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90611
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/291/2019_Meeker_StatisticalMethodsPreprint.pdf|||Fri Jan 14 23:14:19 UTC 2022
dc.subject.disciplines Materials Science and Engineering
dc.subject.disciplines Probability
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Structural Materials
dc.subject.disciplines Structures and Materials
dc.subject.keywords Bayesian inference
dc.subject.keywords Length at detection
dc.subject.keywords MAPOD
dc.subject.keywords POD
dc.subject.keywords Random effects
dc.subject.keywords SHM
dc.title Statistical Methods for Probability of Detection in Structural Health Monitoring
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication a1ae45d5-fca5-4709-bed9-3dd8efdba54e
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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