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

Identyfikatory

DOI

10.24425/bpasts.2023.144606

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

structural health monitoring; wind turbine foundation; damage classification; machine learning; feature extraction; XGBoost

PL

monitorowanie stanu konstrukcji; fundament turbiny wiatrowej; klasyfikacja uszkodzeń; uczenie maszynowe; ekstrakcja cech; XGBoost

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2023
• Tom: Vol. 71, nr 3
• Strony: art. no. e144606
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Leon-Medina Jersson X.

• Control, Data, and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE),Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
• Programa de Ingeniería Mecatrónica, Universidad de San Buenaventura, Carrera 8H #172-20, Bogota, Colombia

• https://orcid.org/0000-0002-9198-1996

autor

Parés Núria

• Laboratori de Càlcul Numèric (LaCàN), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs

• https://orcid.org/0000-0002-2914-9904

autor

Anaya Maribel

• Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia

• https://orcid.org/0000-0002-0241-4771

autor

Tibaduiza Diego A.

• Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia

• https://orcid.org/0000-0002-4498-596X

autor

Pozo Francesc

• Control, Data, and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE),Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
• Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain

• https://orcid.org/0000-0001-8958-6789

Bibliografia

• [1] E. Papatheou, N. Dervilis, A.E. Maguire, C. Campos, I. Antoniadou, and K. Worden, “Performance monitoring of a wind turbine using extreme function theory,” Renew. Energy, vol. 113, pp. 1490–1502, 2017.
• [2] B. Asgarian, V. Aghaeidoost, and H.R. Shokrgozar, “Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform,” Mar. Struct., vol. 45, pp. 1–21, 2016.
• [3] J.X. Leon-Medina, M. Anaya, N. Parés, D.A. Tibaduiza, and F. Pozo, “Structural damage classification in a jacket-type wind turbine foundation using principal component analysis and extreme gradient boosting,” Sensors, vol. 21, no. 8, p. 2748, 2021.
• [4] Y. Vidal, G. Aquino, F. Pozo, and J.E.M. Gutiérrez-Arias, “Structural health monitoring for jacket-type offshore wind turbines: experimental proof of concept,” Sensors, vol. 20, no. 7, p. 1835, 2020.
• [5] B. Puruncajas, Y. Vidal, and C. Tutivén, “Vibration-response-only structural health monitoring for offshore wind turbine jacket foundations via convolutional neural networks,” Sensors, vol. 20, no. 12, p. 3429, 2020.
• [6] E. Hoxha, Y. Vidal, and F. Pozo, “Damage diagnosis for offshore wind turbine foundations based on the fractal dimension,” Appl. Sci., vol. 10, no. 19, p. 6972, 2020.
• [7] M. d. C. Feijóo, Y. Zambrano, Y. Vidal, and C. Tutivén, “Unsupervised damage detection for offshore jacket wind turbine foundations based on an autoencoder neural network,” Sensors, vol. 21, no. 10, p. 3333, 2021.
• [8] J. Baquerizo, C. Tutivén, B. Puruncajas, Y. Vidal, and J. Sampietro, “Siamese neural networks for damage detection and diagnosis of jacket-type offshore wind turbine platforms,” Mathematics, vol. 10, no. 7, p. 1131, 2022.
• [9] D. Agis and F. Pozo, “A frequency-based approach for the detection and classification of structural changes using t-sne,” Sensors, vol. 19, no. 23, p. 5097, 2019.
• [10] D. Agis, D.A. Tibaduiza, and F. Pozo, “Vibration-based detection and classification of structural changes using principal component analysis and-distributed stochastic neighbor embedding,” Struct. Control Health Monit., vol. 27, no. 6, p. e2533, 2020.
• [11] J.X. Leon-Medina, N. Parés, M. Anaya, D.A. Tibaduiza, and F. Pozo, “Data classification methodology for electronic noses using uniform manifold approximation and projection and extreme learning machine,” Mathematics, vol. 10, no. 1, p. 29, 2022.
• [12] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.
• [13] R. Bro and A.K. Smilde, “Principal component analysis,” Anal. Methods, vol. 6, no. 9, pp. 2812–2831, 2014.
• [14] M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput., vol. 15, no. 6, pp. 1373–1396, 2003.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).