Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading
This paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. Subsequent radar measurements are transformed into an image-type representation for use as CNN input. In contrast to conventional approaches th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3337 |
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| author | Erik Streser Sercan Alipek Manuel Rao Jonas Simon Jochen Moll Peter Kraemer Viktor Krozer |
| author_facet | Erik Streser Sercan Alipek Manuel Rao Jonas Simon Jochen Moll Peter Kraemer Viktor Krozer |
| author_sort | Erik Streser |
| collection | DOAJ |
| description | This paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. Subsequent radar measurements are transformed into an image-type representation for use as CNN input. In contrast to conventional approaches that require compensation for temperature and loading effects, the proposed framework inherently learns all required information during the training phase. Its damage detection performance (i.e., detecting intact vs. damaged condition) is demonstrated using measurements from multiple embedded radar sensors during fatigue testing of a wind turbine blade with a length of 31 m. The achieved F1-score for correct damage classification is between 91% and 100% for both the unloaded and the loaded blade. |
| format | Article |
| id | doaj-art-6f404d4fdede4ac2928242b3785d383e |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6f404d4fdede4ac2928242b3785d383e2025-08-20T03:46:49ZengMDPI AGSensors1424-82202025-05-012511333710.3390/s25113337Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue LoadingErik Streser0Sercan Alipek1Manuel Rao2Jonas Simon3Jochen Moll4Peter Kraemer5Viktor Krozer6Department of Physics, Goethe-University Frankfurt, Max-von-Laue Str. 1, 60438 Frankfurt am Main, GermanyDepartment of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, GermanyDepartment of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, GermanyDepartment of Physics, Goethe-University Frankfurt, Max-von-Laue Str. 1, 60438 Frankfurt am Main, GermanyDepartment of Physics, Goethe-University Frankfurt, Max-von-Laue Str. 1, 60438 Frankfurt am Main, GermanyDepartment of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, GermanyDepartment of Physics, Goethe-University Frankfurt, Max-von-Laue Str. 1, 60438 Frankfurt am Main, GermanyThis paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. Subsequent radar measurements are transformed into an image-type representation for use as CNN input. In contrast to conventional approaches that require compensation for temperature and loading effects, the proposed framework inherently learns all required information during the training phase. Its damage detection performance (i.e., detecting intact vs. damaged condition) is demonstrated using measurements from multiple embedded radar sensors during fatigue testing of a wind turbine blade with a length of 31 m. The achieved F1-score for correct damage classification is between 91% and 100% for both the unloaded and the loaded blade.https://www.mdpi.com/1424-8220/25/11/3337structural health monitoringdamage detectionwind turbine bladeFMCW radarmillimeter-waveconvolutional neural network |
| spellingShingle | Erik Streser Sercan Alipek Manuel Rao Jonas Simon Jochen Moll Peter Kraemer Viktor Krozer Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading Sensors structural health monitoring damage detection wind turbine blade FMCW radar millimeter-wave convolutional neural network |
| title | Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading |
| title_full | Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading |
| title_fullStr | Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading |
| title_full_unstemmed | Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading |
| title_short | Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading |
| title_sort | radar based damage detection in a wind turbine blade using convolutional neural networks a proof of concept under fatigue loading |
| topic | structural health monitoring damage detection wind turbine blade FMCW radar millimeter-wave convolutional neural network |
| url | https://www.mdpi.com/1424-8220/25/11/3337 |
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