Stable force identification in structural dynamics using Kalman filtering and dummy-measurements

Naets, Frank; Cuadrado, Javier; Desmet, Wim

doi:10.1016/j.ymssp.2014.05.042

Cited by 183 publications

(148 citation statements)

References 20 publications

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“…As discussed in Simon (), if the matrix

[bold-italicI - G_{i} (\infty) H_{i}] [{boldΨ}_{i} - J_{i} H_{i}]

meets the criterion,

ρ = {(||, η_{j})}_{trueprefixmax} < 1

the Kalman filter for a structural system is stable, where

η_{j}

denotes the eigenvalues of the matrix

[bold-italicI - G_{i} (\infty) H_{i}] [{boldΨ}_{i} - J_{i} H_{i}]

. Generally, the stability of the filter is mainly dependent on the choice of measurements (Naets et al., ). In this study, three kinds of measurements (displacement, velocity, and acceleration) are considered.…”

Section: Structural Response and Wind Load Estimation With Kalman Filtermentioning

confidence: 99%

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Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Zhi

Ming-xin

2016

Computer aided Civil Eng

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This article presents a Kalman‐filter‐based estimation algorithm for identification of wind loads on a super‐tall building using limited structural responses. In practice, acceleration responses are most convenient to be measured among wind‐induced dynamic responses of structures. The proposed inverse method allows estimating the unknown wind loads and structural responses of a super‐tall building using limited acceleration measurements. Taipei 101 Tower is a super‐tall building with 101 stories and a height of 508 m. Field measurements and numerical simulations of the wind effects on Taipei 101 Tower are conducted. The wind loads acting on the super‐tall building are estimated based on the wind‐induced responses determined from the numerical simulations and the refined finite‐element model of the structure, which are in good agreement with the exact results. The stability performance of the proposed algorithm is evaluated. The influence of noise levels in the measurements and covariance matrix of noise on the identification accuracy are investigated and discussed based on the L‐curve method. Finally, the wind loads and structural responses are reconstructed based on the field‐measured accelerations during Typhoon Matsa. The accuracy of the identified results is verified by comparing the reconstructed acceleration responses with the field measurements. The results of this study show that the proposed inverse approach can provide accurate predictions of the wind loads and wind‐induced responses of super‐tall buildings based on limited measured responses.

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“…As discussed in Simon (), if the matrix

[bold-italicI - G_{i} (\infty) H_{i}] [{boldΨ}_{i} - J_{i} H_{i}]

meets the criterion,

ρ = {(||, η_{j})}_{trueprefixmax} < 1

the Kalman filter for a structural system is stable, where

η_{j}

denotes the eigenvalues of the matrix

[bold-italicI - G_{i} (\infty) H_{i}] [{boldΨ}_{i} - J_{i} H_{i}]

Section: Structural Response and Wind Load Estimation With Kalman Filtermentioning

confidence: 99%

“…Naets et al. () suggested an augmented Kalman‐filtering‐based force estimation technique for structural dynamics analysis. To stabilize the identified results based on acceleration measurements, this method proposed to add dummy measurements on the positions of all the degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Zhi

Ming-xin

2016

Computer aided Civil Eng

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“…An alternative for the dual state and input estimation of structural systems was implemented also by Lourens et al [11], using an augmented version of the standard Kalman filter. To improve the poor performance of the latter when acceleration-only measurements are employed, Naets et al [12] proposed a stabilized version of the augmented Kalman filter by using dummy displacement measurements. Finally, in more recent years Azam et al [13] suggested a dual implementation of the Kalman filter in order to resolve the numerical issues that arise in the augmented formulation of input-state estimation problem.…”

Section: Introductionmentioning

confidence: 99%

Reliability Prediction of Fatigue Damage Accumulation on Wind Turbines Support Structures

Tatsis¹,

Chatzi²,

Lourens³

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. The evaluation of fatigue damage accumulation on wind turbine support structures under operational conditions is heavily influenced by a number of uncertainties. These uncertainties may, firstly, be attributed to the highly variable and complex environmental loads, and secondly, to the unavoidable modelling errors which mainly originate from the inherent randomness in both material properties and fatigue resistance of structural components. It is therefore essential that assessment of fatigue life is carried out within a probabilistic framework; one that accounts for the stochastic nature of the phenomenon. The present study proposes a strategy for real-time reliability prediction of accumulated fatigue damage on wind turbine support structures by taking into account the above-mentioned uncertainties. To this end, the availability of structural monitoring information for the identification of the global response on wind-turbine support structures is exploited in order to address the discrepancies between actual and predicted damage accumulation. This is carried through utilization of an augmented version of the Kalman filter, which is capable of jointly estimating the response and the unknown inputs of the structure while relying on a limited number of noisy observations and a presumably uncertain model of the real system. A fixed-lag smoother is further deployed for the attenuation of the estimation error in an on-line mode and the smoothed stochastic estimates of the response are propagated over the model at the level of stresses. The accumulated damage along with the corresponding reliability level is finally predicted using a stochastic nonstationary fatigue damage model. The proposed scheme is demonstrated via implementation on the NREL 5.0 MW wind turbine under different operational conditions, on the basis of dummy vibration data generated via the FAST software.

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“…It is noted that the aforementioned filtering approaches for joint input-state estimation require displacement or strain measurements in addition to acceleration measurements, in order to avoid erroneous low frequency components on the estimated input and states [29]. Following Chatzi and Fuggini [30], Naets et al [31] propose including artificial white noise displacement measurements as observations, in addition to acceleration measurements, to avoid low frequency drift of the estimated input.…”

Section: Introductionmentioning

confidence: 99%

Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

Maes

Nimmen

Lourens

et al. 2016

Mechanical Systems and Signal Processing

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This paper presents a verification of a joint input-state estimation algorithm using data obtained from in situ experiments on a footbridge. The estimation of the input and the system states is performed in a minimum-variance unbiased way, based on a limited number of response measurements and a system model. A dynamic model of the footbridge is obtained using a detailed finite element model that is updated using a set of experimental modal characteristics. The joint inputstate estimation algorithm is used for the identification of two impact, harmonic, and swept sine forces applied to the bridge deck. In addition to these forces, unknown stochastic forces, such as wind loads, are acting on the structure. These forces, as well as measurement errors, give rise to uncertainty in the estimated forces and system states. Quantification of the uncertainty requires determination of the power spectral density of the unknown stochastic excitation, which is identified from the structural response under ambient loading. The verification involves comparing the estimated forces with the actual, measured forces. Although a good overall agreement is obtained between the estimated and measured forces, modeling errors prohibit a proper distinction between multiple forces applied to the structure for the case of harmonic and swept sine excitation.

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Stable force identification in structural dynamics using Kalman filtering and dummy-measurements

Cited by 183 publications

References 20 publications

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Reliability Prediction of Fatigue Damage Accumulation on Wind Turbines Support Structures

Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

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