Browsing by Author "Helsen, Stijn"

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    Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches
    (Frontiers, 2022-09-22) Brijder, Robert; Hagen, Catalina H. M.; Cortés, Ainhoa; Irizar, Andoni; Thibbotuwa, Upeksha Chathurani; Helsen, Stijn; Vásquez, Sandra; Ompusunggu, Agusmian Partogi
    As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models.
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    Switching Kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structures
    (MDPI, 2023-01-05) Brijder, Robert; Helsen, Stijn; Ompusunggu, Agusmian Partogi
    Since 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.