Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/19/6972 |
| _version_ | 1797551701757001728 |
|---|---|
| author | Ervin Hoxha Yolanda Vidal Francesc Pozo |
| author_facet | Ervin Hoxha Yolanda Vidal Francesc Pozo |
| author_sort | Ervin Hoxha |
| collection | DOAJ |
| description | Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage. |
| first_indexed | 2024-03-10T15:49:45Z |
| format | Article |
| id | doaj.art-664c9b802de34c8ab08c79b35565f4c6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:49:45Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-664c9b802de34c8ab08c79b35565f4c62023-11-20T16:08:26ZengMDPI AGApplied Sciences2076-34172020-10-011019697210.3390/app10196972Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal DimensionErvin Hoxha0Yolanda Vidal1Francesc Pozo2Campus Diagonal-Besòs (CDB), Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, SpainCampus Diagonal-Besòs (CDB), Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, SpainCampus Diagonal-Besòs (CDB), Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Eduard Maristany, 16, 08019 Barcelona, SpainCost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage.https://www.mdpi.com/2076-3417/10/19/6972fractal dimensionstructural health monitoringoffshore wind turbine<i>k</i>NNsupport vector machines |
| spellingShingle | Ervin Hoxha Yolanda Vidal Francesc Pozo Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension Applied Sciences fractal dimension structural health monitoring offshore wind turbine <i>k</i>NN support vector machines |
| title | Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension |
| title_full | Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension |
| title_fullStr | Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension |
| title_full_unstemmed | Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension |
| title_short | Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension |
| title_sort | damage diagnosis for offshore wind turbine foundations based on the fractal dimension |
| topic | fractal dimension structural health monitoring offshore wind turbine <i>k</i>NN support vector machines |
| url | https://www.mdpi.com/2076-3417/10/19/6972 |
| work_keys_str_mv | AT ervinhoxha damagediagnosisforoffshorewindturbinefoundationsbasedonthefractaldimension AT yolandavidal damagediagnosisforoffshorewindturbinefoundationsbasedonthefractaldimension AT francescpozo damagediagnosisforoffshorewindturbinefoundationsbasedonthefractaldimension |