Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures
Abstract
:1. Introduction
2. Bayesian Filtering
2.1. Kalman Filtering
2.2. Extensions of Kalman Filtering
3. Proposed Methodology
3.1. Corrosion Detection
3.2. Corrosion Prognosis
3.2.1. Prognosis Algorithms
3.2.2. Wall Thickness Estimation during Steel Corrosion
- Linear corrosion model;
- Power-law corrosion model;
- Bi-modal corrosion model.
3.2.3. Implementation
Linear Corrosion Model
Bi-Modal and Power-Law Corrosion Models
Model Complexitity vs. State Estimation
4. Results
4.1. Performance Metric
4.2. Datasets
4.3. Corrosion Detection
4.4. Corrosion Prognosis
4.5. Accuracy of Remaining Useful Life Estimates
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Prognosis Algorithm | Mean Accuracy |
---|---|
Linear | 0.400 |
Power law | 0.415 |
Bimodal | 0.376 |
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Brijder, R.; Helsen, S.; Ompusunggu, A.P. Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures. Wind 2023, 3, 1-13. https://doi.org/10.3390/wind3010001
Brijder R, Helsen S, Ompusunggu AP. Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures. Wind. 2023; 3(1):1-13. https://doi.org/10.3390/wind3010001
Chicago/Turabian StyleBrijder, Robert, Stijn Helsen, and Agusmian Partogi Ompusunggu. 2023. "Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures" Wind 3, no. 1: 1-13. https://doi.org/10.3390/wind3010001