Full-field strain estimation of subsystems within time-varying and nonlinear systems using modal expansion

Tarpø, Marius; Friis, Tobias; Georgakis, Christos Τ.; Brincker, Rune

doi:10.1016/j.ymssp.2020.107505

Cited by 15 publications

(9 citation statements)

References 28 publications

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“…Since the proposed technique is based on regression, the reader should note that it is similar to the modal expansion technique, [8][9][10][12][13][14][16][17][18] which utilises mode shapes instead of singular vectors. The transformation matrix works as a subspace reduction that removes any noise or response perpendicular to the new subspace of the singular vectors.…”

Section: Data-driven System Modelmentioning

confidence: 99%

“…There, the uncertainty of the estimated strain response was lower than it is typically assumed in a design process; thus, the strain estimation could reduce the number of planned inspections. Tarpø et al 18 showed that the modal expansion technique applies to ‘linear’ subsystem within a nonlinear and time‐varying system with general viscous damping. The concept was proven both numerically and in a laboratory test with two friction‐coupled scaled platforms.…”

Section: Introductionmentioning

confidence: 99%

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Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

et al. 2021

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Virtual sensing enables estimation of stress in unmeasured locations of a system using a system model, physical sensors and a process model. The system model holds the relationship between the physical sensors and the desired stress response. A process model processes both the physical sensors and the system model to synthesise virtual sensors that ‘measure’ the desired stress response. Thus, virtual sensing enables mapping between the physical sensors (input) and the desired stress response (output). The system model is a mathematical model of the system based on knowledge or data of the system. Here, the data‐driven system model is constructed directly on data analyses for the specific system. In this paper, supervised learning and data‐driven system models are applied to strain estimation of an offshore wind turbine in the dynamic range through a novel use of principal component analysis (PCA); 40 min of training data is used to establish the data‐driven system model that can estimate the dynamic strain response with high precision for 2 months, while the estimated fatigue damage averaged out to −1.76% of the measured strain response.

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Section: Data-driven System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

et al. 2021

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“…These methods are referred to as virtual sensing or response estimation/reconstruction [1][2][3][4][5][6][7]. Virtual sensors generate signals by analysing the signals of physical sensors in combination with a process model [5,11]. Virtual sensing techniques can be categorized as analytical/model-based or empirical/datadriven [6].…”

Section: Introductionmentioning

confidence: 99%

Long‐term validation of virtual sensing of a railway bridge with ballasted superstructure

Lorenzen,

Berthold,

Fritzsche

et al. 2023

ce papers

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Railway bridges have a long lifespan, which is challenged by the constant development of vehicles leading to increased loads that they were not originally designed for. To ensure the longest possible use of existing structures, a sensor‐based structural health monitoring system can make a significant contribution. However, due to economic reasons and the inaccessibility of many points of interest, sensors cannot be installed everywhere. Therefore, in most cases, only a few sensors are available at a few points of interest, and methods that aim to reconstruct structural responses at unmeasured points from these measurements are referred to as virtual sensing. In this paper, we have analyzed 19,075 passages recorded on a steel trough bridge with a ballast superstructure and a span of 16.4 m, together with weather data. Our findings show that the influence of train type and speed has a significantly higher impact on the results than environmental factors. The investigation revealed that the model‐based analysis produced similar results to the data‐driven analysis concerning acceleration signals. However, when analyzing strain signals, the two approaches yielded distinctly different results.

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“…Nabuco et al [12] estimated the dynamic full-field strain of an offshore tripod structure during operation with high accuracy. Tarpø et al [13] proved that the modal expansion technique applies to a subsystem within a nonlinear and time-varying system when the nonlinear and time-varying effects act externally on the subsystem. At the end of the 2010s, machine learning and data-driven models enter the portfolio of stress/strain estimation techniques where Lu et al [14] applied pattern recognition and Deng et al [15] applied deep learning to modal expansion.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensing and Strain Estimation on an Offshore Wind Turbine Using Supervised Learning

Tarpø¹,

Amador²,

Katsanos³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Virtual sensing enables expansion and transformation of measured quantities from physical sensors into new quantities at unmeasured locations. This allows for estimating the strain and stress in unmeasured locations of a system by transforming the physical sensors (input) into the desired strain and stress response (output). This transformation model can be based on either knowledge of the systems, data from the system, or any combination of these. In this paper, supervised learning and data-driven models are applied to strain estimation of an offshore wind turbine through Principal Component Analysis (PCA). Training data are used to establish the data-driven model that enables a versatile strain estimation that functions well under different wind scenarios than the training data set.

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Full-field strain estimation of subsystems within time-varying and nonlinear systems using modal expansion

Cited by 15 publications

References 28 publications

Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

Long‐term validation of virtual sensing of a railway bridge with ballasted superstructure

Virtual Sensing and Strain Estimation on an Offshore Wind Turbine Using Supervised Learning

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