Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures

• Published in: Wind Vol. 3; no. 1; pp. 1 - 13
• Main Authors: Brijder, Robert, Helsen, Stijn, Ompusunggu, Agusmian Partogi
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-03-2023
• Subjects: Bayesian filtering; corrosion; diagnosis; offshore wind turbines; prognosis
• ISSN: 2674-032X; 2674-032X
• DOI: 10.3390/wind3010001

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.