Publication:
Numerical calibration of direct current potential drop measuring: A comparison of FEM- and Bayesian filtering-based approaches

dc.contributor.authorBerg, Thomas
dc.contributor.authorEnde, Sven von
dc.contributor.authorLammering, Rolf
dc.date.issued2018
dc.description.abstractThe direct current potential drop method is a widespread non-destructive testing and evaluation technique utilised especially in shipbuilding and aerospace industry. Even without feasibility of optical assessment it allows for the accurate health monitoring of electrically conductive components and structures. At anticipated damage locations, the drop of the electric potential of an injected electrical current is measured and subsequently used to determine the respective damage extent on the basis of a specific calibration curve. Though knowledge on this specimen-bound relation between potential drop and damage extent is crucial, the experimental calibration approaches either entail considerable efforts and limitations in the test setup or ensue only afterwards. As opposed to this, FEM-based numerical calibration allows for a real-time damage extent monitoring, however only in a generalised form since no actual measurement data are incorporated into the process. Recent studies propose a new calibration methodology by means of Bayesian filtering where the damage extent is inferred probabilistically from potential drop measurements for fatigue loading conditions. The present work aims to provide a comparison of the aforementioned numerical approaches and their benefits and drawbacks. © 2018 NDT.net. All rights reserved.
dc.description.versionNA
dc.identifier.urihttps://openhsu.ub.hsu-hh.de/handle/10.24405/4439
dc.language.isoen
dc.publisherNDT.net
dc.relation.conference9th European Workshop on Structural Health Monitoring, EWSHM 2018
dc.relation.orgunitMechanik
dc.rights.accessRightsmetadata only access
dc.subjectAerospace Industry
dc.subjectElectric Potential
dc.subjectFinite Element Method
dc.titleNumerical calibration of direct current potential drop measuring: A comparison of FEM- and Bayesian filtering-based approaches
dc.typeConference paper
dspace.entity.typePublication
hsu.uniBibliography
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