Damage detection of TLP and Spar floating wind turbine using dynamic response of the structure

Jamalkia, Aysan; Ettefagh, Mir Mohammad; Mojtahedi, Alireza

doi:10.1016/j.oceaneng.2016.08.009

Cited by 45 publications

(21 citation statements)

References 15 publications

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“…These expenses are significantly increased in offshore platforms [38]. Therefore, the use of SHM as a tool to prevent sudden damage yields essential benefits in this industry [39].…”

Section: Economic Justificationmentioning

confidence: 99%

“…Their approach enabled the calculation of changes in the form of the structure and the differentiation of possible failures. Other closely related works have presented dynamic models of the behavior of this type of structure; these models include the relationship between ocean currents under the ocean and wind currents on the surface, which may affect the analysis of the captured data [39]. Oliveira et al [58] show the implementation of modal analysis in turbines based on vibration, building strategies to reduce the influence of EOVs through statistical analysis.…”

Section: Operational and Environmental Conditionsmentioning

confidence: 99%

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Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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“…These expenses are significantly increased in offshore platforms [38]. Therefore, the use of SHM as a tool to prevent sudden damage yields essential benefits in this industry [39].…”

Section: Economic Justificationmentioning

confidence: 99%

Section: Operational and Environmental Conditionsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…According to Figure 27 In order to investigate the performance of the proposed method in the presence of environmental noise, the white Gaussian noise is added to the testing data with different signal-to-noise (SNR) values for more realistic assumptions. SNR is defined as follows 9,[51][52][53] SNR dB = 10 log 10 P signal P noise ð27Þ…”

Section: Damage Detection Of the Lab-scale Offshore Jacket Modelmentioning

confidence: 99%

Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

Mousavi

Varahram

Ettefagh

et al. 2020

Structural Health Monitoring

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Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

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“…Offshore structures operate under varying Environmental and Operating Conditions (EOCs) such as wind speed (WS), significant wave height (SWH), current, temperature and others, which partially or fully "mask" the effects of damages on the structural dynamics, rendering SHM highly challenging [1][2][3][4]. The SHM of tendons [5][6][7][8], mooring lines [9][10][11][12][13], offshore platforms [14][15][16][17][18][19][20][21] and OWT towers [21][22][23], has been mostly investigated albeit under constant EOCs. However, the realistic case of varying EOCs that significantly affects the structural dynamics and makes SHM difficult, has been considered only in a limited number of studies [1][2][3][24][25] with focus mostly on platforms and towers of OWTs.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring of tendons in a multibody floating offshore wind turbine under varying environmental and operating conditions

et al. 2021

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Damage detection of TLP and Spar floating wind turbine using dynamic response of the structure

Cited by 45 publications

References 15 publications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

Structural health monitoring of tendons in a multibody floating offshore wind turbine under varying environmental and operating conditions

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