Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties

Hu, Wei; Thöns, Sebastian; Rohrmann, R.; Said, Samir; Rücker, Werner

doi:10.1016/j.engstruct.2014.12.035

Cited by 70 publications

(36 citation statements)

References 19 publications

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“…where Σ i is a diagonal matrix of dimensions mi × j; U i and V i are orthogonal matrices of dimensions mi × mi and mi × j, respectively; Σ i1 and Σ i2 are submatrices of Σ i that have dimensions 2n × 2n and (mi À 2n) × (j À 2n), respectively, in which n is the degree of freedom of the system; U i1 and U i2 are submatrices of U i ; and V i1 and V i2 submatrices of V i . Relations between equations (6) and (7) can be expressed by…”

Section: Classical Stochastic Subspace Identification Methodsmentioning

confidence: 99%

“…With consideration of dynamic features of wind turbines, vibrationbased monitoring methods have been studied for structural assessment and life-cycle management. [4][5][6][7][8] Accurate modal parameter identification is one of the critical tasks in vibration-based structural health monitoring. 9,10 Operational modal analysis (OMA) is an effective tool for modal parameter identification.…”

Section: Introductionmentioning

confidence: 99%

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Development of a modified stochastic subspace identification method for rapid structural assessment of in‐service utility‐scale wind turbine towers

et al. 2017

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The strong drive to harness wind energy has recently led to rapid growth of wind farm construction. Wind turbine towers with increased sizes and flexibility experience large vibrations. Structural health monitoring of wind turbines is proposed in the wind energy industry to ensure their proper performance and save maintenance costs. This study proposes a system identification method for vibration-based structural assessment of wind turbine towers. This method developed based on the stochastic subspace identification method can identify modal parameters of structures in operating conditions with harmonic components in excitations. It benefits wind turbine tower structural health assessment because classical operational modal analysis methods can fail as periodic rotation excitation from a turbine introduces harmonic disturbance to tower structure response data. The effectiveness, accuracy and robustness of the proposed method were numerically investigated and verified through a lumped-mass system model. The method was then applied to an in-service utilityscale wind turbine tower. The field testing campaign and modal parameter identification as well as structural assessment results were presented.

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Section: Classical Stochastic Subspace Identification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a modified stochastic subspace identification method for rapid structural assessment of in‐service utility‐scale wind turbine towers

et al. 2017

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“…The consequences that the variations in the EOC have on the dynamic behavior of structures were assessed in different studies [119,128]. For example, a statistical methodology that propagates variability in measured Frequency Response Function data and calculates the level of uncertainty of the modal properties is explained in [129].…”

Section: Variation In Environmental and Operational Conditionsmentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

217

124

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“…Structural health monitoring (SHM) has become a popular tool for safety and serviceability assessment of in-service civil structural systems such as buildings and bridges. Significant efforts have been made over the last two decades toward the development of sensing technologies, 1,2 data processing techniques, 3,4 computational modeling, and model updating [5][6][7][8] as well as system identification algorithms [9][10][11] for SHM in both lab experiments and field testing. Recently, monitoring of building structures has gained tremendous attention.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of a unique tower in Kuwait for structural health monitoring: Numerical investigations

Sun

Al-Qazweeni

Parol

et al. 2019

Struct Control Health Monit

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Summary Computational modeling, in addition to data analytics, plays an important role in structural health monitoring (SHM). The high‐fidelity computational model based on the design and construction information provide important dynamics information of the structure and, more importantly, can be updated against field measurements for SHM purposes such as damage detection, response prediction, and reliability assessment. In this paper, we present a unique skyscraper (Al‐Hamra Tower) located in Kuwait City and its high‐fidelity computational model using ETABS for structural health monitoring applications. The tower is made of cast‐in‐place reinforced concrete with a core of shear walls and two curved shear walls running the height of the building (approximately 413 m with 86 floors in total). Interesting static and dynamic characteristics of the tower are described. System identification, interferometry‐based wave propagation analysis, and wave‐based damage detection are performed using synthetic data. This work mainly presents the phase of numerical investigations, which serves as a basis for correlating the field monitoring data to the model of the building in future work.

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Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties

Cited by 70 publications

References 19 publications

Development of a modified stochastic subspace identification method for rapid structural assessment of in‐service utility‐scale wind turbine towers

Development of a modified stochastic subspace identification method for rapid structural assessment of in‐service utility‐scale wind turbine towers

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Computational modeling of a unique tower in Kuwait for structural health monitoring: Numerical investigations

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