Switching Kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structures

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dc.contributor.authorBrijder, Robert
dc.contributor.authorHelsen, Stijn
dc.contributor.authorOmpusunggu, Agusmian Partogi
dc.date.accessioned2023-01-09T11:32:52Z
dc.date.available2023-01-09T11:32:52Z
dc.date.issued2023-01-05
dc.description.abstractSince manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose an algorithm for corrosion detection and three algorithms for corrosion prognosis by using Bayesian filtering approaches, and quantitatively compare their accuracy against synthetic datasets having characteristics typical for wall thickness measurements using ultrasound sensors. We found that a corrosion prognosis algorithm based on the Pourbaix corrosion model using unscented Kalman filtering outperforms the algorithms based on a linear corrosion model and the bimodal corrosion model introduced by Melchers.en_UK
dc.identifier.citationBrijder R, Helsen S, Ompusunggu AP. (2023) Switching Kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structures. Wind, Volume 3, Issue 1, January 2023, pp. 1-13en_UK
dc.identifier.issn2674-032X
dc.identifier.urihttps://doi.org/10.3390/wind3010001
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18904
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcorrosionen_UK
dc.subjectdiagnosisen_UK
dc.subjectprognosisen_UK
dc.subjectoffshore wind turbinesen_UK
dc.subjectBayesian filteringen_UK
dc.titleSwitching Kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structuresen_UK
dc.typeArticleen_UK

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