Damage detection in mooring systems of floating offshore wind turbines using semi-supervised GAN with image-transformed limited labelled data.
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| Title: | Damage detection in mooring systems of floating offshore wind turbines using semi-supervised GAN with image-transformed limited labelled data. |
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| Authors: | Tamuly, Pranjal1 (AUTHOR) ptamuly@bcamath.org, Nava, Vincenzo2 (AUTHOR) |
| Source: | Advances in Structural Engineering. Jul2026, Vol. 29 Issue 10, p1954-1972. 19p. |
| Subjects: | Generative adversarial networks, Image representation, Supervised learning, Offshore wind power plants, Mooring of ships, Deep learning, Fault diagnosis |
| Abstract: | Detection of damage in the mooring systems of Floating Offshore Wind Turbines (FOWTs) is essential to guarantee operational reliability and reduce corrective maintenance costs. However, the complex nature of environmental conditions, the high costs of data collection, and the rarity of damage events make it challenging to obtain extensive labelled datasets. As a result, addressing damage detection from limited labelled data is necessary, yet it remains a relatively under-explored area in the literature. To tackle these challenges, this paper introduces an image-transformed semi-supervised generative adversarial network (ITSGAN) technique based on deep generative models. The method transforms time series data into multichannel image representations, enabling deep learning models to more effectively capture both spatial and temporal features. By combining adversarial training with supervised learning, ITSGAN leverages both labelled and unlabelled data to improve damage detection ability, particularly in scenarios where labelled data is scarce. A comparative analysis with established models such as traditional semi-supervised GAN, Deep Convolutional Neural Networks (DCNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB) shows that ITSGAN consistently outperforms these models in accuracy, precision, recall, and F1 score. It is also demonstrated that the proposed ITSGAN model preserves richer feature representations by transforming time series data into images, resulting in enhanced performance in damage detection tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194727733 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Damage detection in mooring systems of floating offshore wind turbines using semi-supervised GAN with image-transformed limited labelled data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tamuly%2C+Pranjal%22">Tamuly, Pranjal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ptamuly@bcamath.org</i><br /><searchLink fieldCode="AR" term="%22Nava%2C+Vincenzo%22">Nava, Vincenzo</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Structural+Engineering%22">Advances in Structural Engineering</searchLink>. Jul2026, Vol. 29 Issue 10, p1954-1972. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Image+representation%22">Image representation</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Offshore+wind+power+plants%22">Offshore wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Mooring+of+ships%22">Mooring of ships</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Detection of damage in the mooring systems of Floating Offshore Wind Turbines (FOWTs) is essential to guarantee operational reliability and reduce corrective maintenance costs. However, the complex nature of environmental conditions, the high costs of data collection, and the rarity of damage events make it challenging to obtain extensive labelled datasets. As a result, addressing damage detection from limited labelled data is necessary, yet it remains a relatively under-explored area in the literature. To tackle these challenges, this paper introduces an image-transformed semi-supervised generative adversarial network (ITSGAN) technique based on deep generative models. The method transforms time series data into multichannel image representations, enabling deep learning models to more effectively capture both spatial and temporal features. By combining adversarial training with supervised learning, ITSGAN leverages both labelled and unlabelled data to improve damage detection ability, particularly in scenarios where labelled data is scarce. A comparative analysis with established models such as traditional semi-supervised GAN, Deep Convolutional Neural Networks (DCNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB) shows that ITSGAN consistently outperforms these models in accuracy, precision, recall, and F1 score. It is also demonstrated that the proposed ITSGAN model preserves richer feature representations by transforming time series data into images, resulting in enhanced performance in damage detection tasks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/13694332251391469 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1954 Subjects: – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Image representation Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Offshore wind power plants Type: general – SubjectFull: Mooring of ships Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Fault diagnosis Type: general Titles: – TitleFull: Damage detection in mooring systems of floating offshore wind turbines using semi-supervised GAN with image-transformed limited labelled data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tamuly, Pranjal – PersonEntity: Name: NameFull: Nava, Vincenzo IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13694332 Numbering: – Type: volume Value: 29 – Type: issue Value: 10 Titles: – TitleFull: Advances in Structural Engineering Type: main |
| ResultId | 1 |
