- Tytuł:
- Ensemble of feature extraction methods to improve the structural damage classification in a wind turbine foundation
- Autorzy:
-
Leon-Medina, Jersson X.
Parés, Núria
Anaya, Maribel
Tibaduiza, Diego A.
Pozo, Francesc - Powiązania:
- https://bibliotekanauki.pl/articles/27311417.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Czasopisma i Monografie PAN
- Tematy:
-
structural health monitoring
wind turbine foundation
damage classification
machine learning
feature extraction
XGBoost
monitorowanie stanu konstrukcji
fundament turbiny wiatrowej
klasyfikacja uszkodzeń
uczenie maszynowe
ekstrakcja cech - Opis:
- The condition monitoring of offshore wind power plants is an important topic that remains open. This monitoring aims to lower the maintenance cost of these plants. One of the main components of the wind power plant is the wind turbine foundation. This study describes a data-driven structural damage classification methodology applied in a wind turbine foundation. A vibration response was captured in the structure using an accelerometer network. After arranging the obtained data, a feature vector of 58 008 features was obtained. An ensemble approach of feature extraction methods was applied to obtain a new set of features. Principal Component Analysis (PCA) and Laplacian eigenmaps were used as dimensionality reduction methods, each one separately. The union of these new features is used to create a reduced feature matrix. The reduced feature matrix is used as input to train an Extreme Gradient Boosting (XGBoost) machine learning-based classification model. Four different damage scenarios were applied in the structure. Therefore, considering the healthy structure, there were 5 classes in total that were correctly classified. Five-fold cross validation is used to obtain a final classification accuracy. As a result, 100% of classification accuracy was obtained after applying the developed damage classification methodology in a wind-turbine offshore jacket-type foundation benchmark structure.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 3; art. no. e144606
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł