Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Leon-Medina, Jersson X.; Anaya, Maribel; Mariné, Núria Parés; Burgos, Diego Alexander Tibaduiza; Pozo, Francesc

doi:10.3390/s21082748

Cited by 16 publications

(16 citation statements)

References 46 publications

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“…To verify the advantages of our proposed method, classical ML algorithms including support vector machine (SVM) [34], FFT-SVM, random forest (RF) [35], K-nearest neighbor (KNN) [36], and eXtreme gradient boosting (XGBoost) [37] are selected to evaluate structural damage degree and improved accuracy in structural damage detection. The accuracy of KNN, RF, and XGBoost is 67.64%, 70.24%, and 75.78%, respectively, representing a low ability for recognizing structural damage detection.…”

Section: Compared With Other Methodsmentioning

confidence: 99%

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Chen

Liu

et al. 2021

Applied Sciences

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In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

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Section: Compared With Other Methodsmentioning

confidence: 99%

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Chen

Liu

et al. 2021

Applied Sciences

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“… Location of the sensors (accelerometers) in the structure [ 19 ]. …”

Section: Figurementioning

confidence: 99%

“… Damage location in the different levels of the jacket structure ( left ), [ 19 ]. Crack damage where L is the length of the bar, d = 5 mm is the crack size, and

is the location of the crack in the bar ( right ).…”

Section: Figurementioning

confidence: 99%

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Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Feijóo

Zambrano

Vidal

et al. 2021

Sensors

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Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

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“…One popular multivariate statistical method is the principal component analysis (PCA), which allows for classification of variables or individuals [9,10]. The PCA has been used in several studies related to COVID-19 [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data

Martín-Barreiro

Ramirez-Figueroa

Cabezas

et al. 2021

Sensors

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In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.

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Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Cited by 16 publications

References 46 publications

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data

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