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A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties

A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties
A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties

Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher’s linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members’ integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring of an offshore wind jacket structure.

2332-9017
Cevasco, Debora
99f4f8af-2bbc-49cb-8dfb-2d7db120c41b
Tautz-Weinert, Jannis
ffe6bb8d-bf04-4270-a0d2-4d305b819fbb
Richmond, Mark
26b56dcd-d6b3-4b36-b9a9-04f286f082f6
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Kolios, Athanasios
8db8544e-cdd1-43ee-aa2d-780f67c1f15b
Cevasco, Debora
99f4f8af-2bbc-49cb-8dfb-2d7db120c41b
Tautz-Weinert, Jannis
ffe6bb8d-bf04-4270-a0d2-4d305b819fbb
Richmond, Mark
26b56dcd-d6b3-4b36-b9a9-04f286f082f6
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Kolios, Athanasios
8db8544e-cdd1-43ee-aa2d-780f67c1f15b

Cevasco, Debora, Tautz-Weinert, Jannis, Richmond, Mark, Sobey, Adam and Kolios, Athanasios (2022) A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties. Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 8 (2), [021103]. (doi:10.1115/1.4053659).

Record type: Article

Abstract

Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher’s linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members’ integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring of an offshore wind jacket structure.

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More information

Accepted/In Press date: 20 December 2021
e-pub ahead of print date: 7 March 2022
Published date: 1 June 2022
Additional Information: Funding Information: • University of Strathclyde (Grant No. EP/L016303/1; Funder ID: 10.13039/100008078). Funding Information: • European Union’s Horizon 2020 (Grant No. 745625; Funder ID: H2020-LCE-2016-RES-IA). Publisher Copyright: Copyright © 2022 by ASME.

Identifiers

Local EPrints ID: 454197
URI: http://eprints.soton.ac.uk/id/eprint/454197
ISSN: 2332-9017
PURE UUID: fac21a24-7d0e-43ee-902a-ad9f6aac02d4
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338

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Date deposited: 02 Feb 2022 17:37
Last modified: 02 Feb 2023 02:40

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Contributors

Author: Debora Cevasco
Author: Jannis Tautz-Weinert
Author: Mark Richmond
Author: Adam Sobey ORCID iD
Author: Athanasios Kolios

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