Damage diagnosis for offshore ﬁxed wind turbines

Agis, David; Vidal, Yolanda; Pozo, Francesc

doi:10.24084/repqj17.313

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…Using the constructed frequency response , or ℎ , , the modes of the virtual structure with the added virtual mass can be identified as the indicators for damage identification. For a virtual structure with an added virtual mass ∆ m , its frequency response Figure 1 can be calculated using Equation (11), which is a simplified form of Equation (10), as deduced in detail in [42]:…”

Section: Additional Virtual Mass Methodsmentioning

confidence: 99%

“…In practice, for complex structures such as bridges, it is generally feasible to divide them into substructures, and to identify the substructural damage. For each substructure, a virtual mass is applied, and then the natural frequencies of the corresponding virtual structures are identified using the structural frequency responses h v ∆ (ω, m v ) constructed via Equation (11). Denote by ω m ij as the identified i-th frequency of the virtual structure with the virtual mass applied to the j-th substructure.…”

Section: Damage Identificationmentioning

confidence: 99%

“…The damage extents and locations can be accurately identified. The value of the virtual additional mass, i.e., ∆ m = 4.0 × 10 4 kg, is substituted into Equation (11). The frequency responses of the corresponding virtual structure can be constructed, see Figure 15 'VDM'.…”

Section: Damage Identificationmentioning

confidence: 99%

“…Structural health monitoring (SHM) [3][4][5][6][7][8] has been extensively employed in practical civil engineering projects, especially for large-scale structures such as bridges [9,10]. Structural damage identification [11][12][13][14][15] has an important role in providing effective evidence for bridge maintenance and assessment.…”

Section: Introductionmentioning

confidence: 99%

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Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

Zhang

Hou

Jankowski

2020

Sensors

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Structural damage identification plays an important role in providing effective evidence for the health monitoring of bridges in service. Due to the limitations of measurement points and lack of valid structural response data, the accurate identification of structural damage, especially for large-scale structures, remains difficult. Based on additional virtual mass, this paper presents a damage identification method for bridges using a vehicle bump as the excitation. First, general equations of virtual modifications, including virtual mass, stiffness, and damping, are derived. A theoretical method for damage identification, which is based on additional virtual mass, is formulated. The vehicle bump is analyzed, and the bump-induced excitation is estimated via a detailed analysis in four periods: separation, free-fall, contact, and coupled vibrations. The precise estimation of bump-induced excitation is then applied to a bridge. This allows the additional virtual mass method to be used, which requires knowledge of the excitations and acceleration responses in order to construct the frequency responses of a virtual structure with an additional virtual mass. Via this method, a virtual mass with substantially more weight than a typical vehicle is added to the bridge, which provides a sufficient amount of modal information for accurate damage identification while avoiding the bridge overloading problem. A numerical example of a two-span continuous beam is used to verify the proposed method, where the damage can be identified even with 15% Gaussian random noise pollution using a 1-degree of freedom (DOF) car model and 4-DOF model.

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Section: Additional Virtual Mass Methodsmentioning

confidence: 99%

Section: Damage Identificationmentioning

confidence: 99%

Section: Damage Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

Zhang

Hou

Jankowski

2020

Sensors

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

Leon-Medina

Anaya

Mariné

et al. 2021

Sensors

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Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

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Damage diagnosis for offshore ﬁxed wind turbines

Cited by 2 publications

References 18 publications

Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

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

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