Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms

Maes, Kristof; Iliopoulos, Alexandros; Weijtjens, Wout; Devriendt, Christof; Lombaert, Geert

doi:10.1016/j.ymssp.2016.01.004

Cited by 133 publications

(80 citation statements)

References 42 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additional accelerometers at the tower or transition piece are not always present. Maes et al (2016) showed that the first and second fore-aft and side-side natural frequencies of a monopile are identifiable from strain gauge measurements at the tower in operating conditions of the wind turbine by transforming strain time series into power spectral densities.…”

Section: Fe Model Updatingmentioning

confidence: 99%

“…They try to reproduce the time history of dynamic response parameters, such as acceleration or strain, of the whole structure. This has been investigated for monopiles using Kalman filters (Maes et al, 2016;Fallais et al, 2016), joint input-state estimation (Maes et al, 2016), and modal expansion algorithms (Maes et al, 2016;Iliopoulos et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

et al. 2017

View full text Add to dashboard Cite

Abstract. Operators need accurate knowledge on structural reserves to decide about lifetime extension of offshore wind turbines. Load monitoring enables us to directly compare design loads with real loading histories of the support structure in order to calculate its remaining useful lifetime. Monitoring of every hot spot is technically and financially not feasible. This paper presents a novel idea for load monitoring of monopiles. It requires strain measurements at only one level convenient for sensor installation, such as tower bottom. Measurements are converted into damage equivalent loads for 10 min time intervals. Damage equivalent loads are extrapolated to other locations of the structure with a simulation model and statistical algorithm. For this, structural loads at all locations of the monopile are calculated with aero-hydro-elastic software and updated finite element models. Damage equivalent loads at unmeasured locations are predicted from the simulation results with a k-nearest neighbor regression algorithm. The extrapolation was tested with numerical simulations of an 8 MW offshore wind turbine. Results show that damage can be predicted with an error of 1-3 % if this is done conditional on mean wind speed, which is very promising. The load monitoring concept is simple, cheap and easy to implement. This makes it ideal for making decisions on lifetime extension of monopiles.

show abstract

Section: Fe Model Updatingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Moreover, in the railway transportation, the estimation of the wheel–rail contact forces is crucial to assess rolling contact fatigue and damage detection on the track as well as to certify the vehicle design safety . In addition to the input identification, the state estimation has also received very much attention in the structural dynamics to predict stress and fatigue life, to identify damage, and to determine the response in inaccessible points …”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12] In addition to the input identification, the state estimation has also received very much attention in the structural dynamics to predict stress and fatigue life, to identify damage, and to determine the response in inaccessible points. [13][14][15][16][17][18][19][20][21][22] As a matter of fact, it is not feasible directly to measure the exposed load by transducers when we have not access to the location and sometimes when economic factors are important; however, the transient response at some points are captured utilizing various sensors (strain gauge, accelerometer, and displacement transducer). Therefore, majority of engineers and researchers prefer to employ one of indirect methods to identify dynamic loads in such cases.…”

Section: Introductionmentioning

confidence: 99%

Bayesian framework for simultaneous input/state estimation in structural and mechanical systems

Valikhani

Younesian

2019

Struct Control Health Monit

View full text Add to dashboard Cite

Summary Real‐time input/state estimation by means of the observed response has drawn considerable attention in the structural, dynamic, and control systems. In this line, a minimum‐variance unbiased (MVU) estimator has been utilized to develop three‐step filters that sometimes experience instability and divergence, especially for the input estimation in structural dynamics. To solve this challenge, this study is aimed at to estimate both the input and state simultaneously for a linear system. A Bayesian framework is developed, in which the prior information of the unknown input in terms of the a priori probability density function is incorporated in estimation process. Formulation of the estimation is presented in the state‐space representation in presence and absence of the direct feedthrough. Three‐step filters including input estimation, state prediction, and the updating are developed for the system having model uncertainty and noisy observed data. In addition to the estimation, another novel feature of this study is to predict the steady states of the filters in the closed‐form representation. We also establish sufficient conditions for convergence and stability of the proposed filters. At the end, the efficiency, robustness, and advantage of the approach is verified through numerical studies for different systems. Results for the input and state estimation are compared with real values according to the MVU estimator for the observed noisy data sets.

show abstract

“…A technique was proposed by Baqersad et al [11][12][13] to expand the limited set of real-time displacement data and apply it to a finite element model to extract full-field strain data. Iliopoulos et al [14,15] proposed an approach for expanding accelerometer data for fatigue analysis. Researchers have also used a Confluence Algorithm [16] for full field response prediction.…”

Section: Introductionmentioning

confidence: 99%

Modal Expansion using Strain Mode Shapes

Baqersad

Bharadwaj

Poozesh

2017

Shock &Amp; Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics &Amp; Optics, Volume 9

View full text Add to dashboard Cite

Reduction/expansion approaches have been conventionally used in correlation and validation studies. Recently, these approaches have also been used to extract full-filed results on structures using limited set of data measured. The expansion techniques are used to expand real-time data measured on components of vehicle chassis, utility scale wind turbines, and helicopter rotors. The resulted full-field data is used to monitor structures and determine their durability. With the advances in Digital Image Correlation (DIC), researchers are able to readily extract strain mode shapes of structures. However, the conventional reduction/expansion techniques are usually limited to displacement, velocity, or acceleration data. This can hinder correlation studies that compare the strain data between two models. Furthermore, this limitation does not allow researchers to expand strain-gage measured data for full-field structural monitoring and durability analysis.In the current paper, a reduction/expansion technique has been developed to reduce/expand strain data. In this technique, measured strain at limited locations is expanded using strain mode shapes to extract full-field results on the entire surface and within the structure. This technique can also be used to reduce data for correlation studies. In order to demonstrate the merit of the approach, the proposed strain expansion approach was applied to the finite element model of a cantilever beam subjected to sinusoidal and impact excitations. The results show that the proposed approach can effectively expand the strain measured at limited locations. The approach could accurately predict the strain at locations where no sensors were placed. Keywords Modal expansion • Strain mode shapes • Structural health monitoring • Modal analysis • Expansion methodology Nomenclature S nFull-space strain vector RTOS a Real-time operating strain in reduced-space V n Full-space strain mode shape matrix U n Full-space displacement mode shape matrix T Transformation matrix X n Full-space displacement vector RTOS en Real-time operating strain in full-space V a Reduced-space strain mode shape matrix P Mode contribution vector V g a

show abstract

Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms

Cited by 133 publications

References 42 publications

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?

Bayesian framework for simultaneous input/state estimation in structural and mechanical systems

Modal Expansion using Strain Mode Shapes

Contact Info

Product

Resources

About