Details
| Translated title of the contribution | Methoden Monitoring of Tower Structures with Probabilistic Methods |
|---|---|
| Original language | German |
| Title of host publication | Baudynamik 2025 |
| Pages | 135-146 |
| Number of pages | 12 |
| ISBN (electronic) | 9783181024478 |
| Publication status | Published - 2025 |
Publication series
| Name | VDI-Berichte |
|---|---|
| Number | 2447 |
| Volume | 2025 |
| ISSN (Print) | 0083-5560 |
Abstract
As the hub height of wind turbines increases, so do the demands for monitoring the structural integrity their tower structures. Vibration-based structural health monitoring often employs operational modal analysis methods, such as Bayesian Operational Modal Analysis (BAYOMA). This study investigates the impact of closely spaced modes of tower structures on identification uncertainties. It is shown that both identification and identification uncertainties are subject to operational influences. Consequently, heteroscedastic Gaussian Processes (GP) offer robust data normalization that accounts for this input-dependent variance. A newly developed probabilistic novelty metric combines the identification uncertainties from BAYOMA with the regression uncertainties from the GP for condition assessment. The described approach is tested on the tower structure of an operating wind turbine.
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Baudynamik 2025. 2025. p. 135-146 (VDI-Berichte; Vol. 2025, No. 2447).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research
}
TY - CHAP
T1 - Monitoring von Turmstrukturen mit probabilistischen Methoden
T2 - Monitoring of Tower Structures with Probabilistic Methods
AU - Jonscher, C.
AU - Grießmann, T.
AU - Rolfes, R.
N1 - Publisher Copyright: © 2025, VDI Verlag GMBH. All rights reserved.
PY - 2025
Y1 - 2025
N2 - As the hub height of wind turbines increases, so do the demands for monitoring the structural integrity their tower structures. Vibration-based structural health monitoring often employs operational modal analysis methods, such as Bayesian Operational Modal Analysis (BAYOMA). This study investigates the impact of closely spaced modes of tower structures on identification uncertainties. It is shown that both identification and identification uncertainties are subject to operational influences. Consequently, heteroscedastic Gaussian Processes (GP) offer robust data normalization that accounts for this input-dependent variance. A newly developed probabilistic novelty metric combines the identification uncertainties from BAYOMA with the regression uncertainties from the GP for condition assessment. The described approach is tested on the tower structure of an operating wind turbine.
AB - As the hub height of wind turbines increases, so do the demands for monitoring the structural integrity their tower structures. Vibration-based structural health monitoring often employs operational modal analysis methods, such as Bayesian Operational Modal Analysis (BAYOMA). This study investigates the impact of closely spaced modes of tower structures on identification uncertainties. It is shown that both identification and identification uncertainties are subject to operational influences. Consequently, heteroscedastic Gaussian Processes (GP) offer robust data normalization that accounts for this input-dependent variance. A newly developed probabilistic novelty metric combines the identification uncertainties from BAYOMA with the regression uncertainties from the GP for condition assessment. The described approach is tested on the tower structure of an operating wind turbine.
UR - http://www.scopus.com/inward/record.url?scp=105007200604&partnerID=8YFLogxK
U2 - 10.51202/9783181024478-135
DO - 10.51202/9783181024478-135
M3 - Beitrag in Buch/Sammelwerk
SN - 978-3-18-092447-2
T3 - VDI-Berichte
SP - 135
EP - 146
BT - Baudynamik 2025
ER -