Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

Maes, Kristof; Lourens, Eliz-Mari; Nimmen, Katrien Van; Reynders, Edwin; Roeck, Guido De; Lombaert, Geert

doi:10.1016/j.ymssp.2014.07.018

Cited by 90 publications

(90 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an alternative to models based on first principles, models can be directly identified from experimental vibration data using system identification techniques, see for example [32,33]. Throughout the derivation of the algorithm, it is assumed that the sensor network meets the conditions for instantaneous system inversion derived in [29]. The process noise vector w [k] ∈ R ns and measurement noise vector v [k] ∈ R n d both account for unknown excitation sources and modeling errors.…”

Section: Joint Input-state Estimation Algorithmmentioning

confidence: 99%

“…If the conditions for instantaneous system inversion presented in [29] are satisfied, the joint input-state estimation algorithm presented in Section 2.1 is stable and the gain matrices M [k] and K [k] , as well as the error covariance matrices…”

Section: Quantification Of Estimation Uncertaintymentioning

confidence: 99%

“…During the experiments, the acceleration response of the footbridge has been recorded in three orthogonal directions at 12 locations on the bridge deck. In addition, the vertical displacement of the bridge deck has been recorded at two locations, as displacement data are needed to ensure the stability of the joint input-state estimation algorithm and the uniqueness of the force estimates obtained [29].…”

Section: Measurement Setupmentioning

confidence: 99%

“…Determination of the sensor configuration for force identification requires the meeting of the conditions for instantaneous system inversion [29]. The invertibility of a linear system model in general depends on three conditions.…”

Section: Selection Of Data For Force Identificationmentioning

confidence: 99%

“…Alternatively, a classical Kalman filter can be used to jointly estimate the dynamic forces and system states, thus including the unknown forces in an augmented state vector [28]. 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%

See 4 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Joint Input-state Estimation Algorithmmentioning

confidence: 99%

Section: Quantification Of Estimation Uncertaintymentioning

confidence: 99%

Section: Measurement Setupmentioning

confidence: 99%

Section: Selection Of Data For Force Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

show abstract

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

Zhao

2020

Struct Control Health Monit

View full text Add to dashboard Cite

Summary Towards a complete and accurate seismic assessment of a super high‐rise building under bidirectional long‐period ground motions, this study proposes an optimal multi‐type sensor placement framework for the best reconstruction of structural responses and ground motions based on a full‐scale 3D finite element (FE) model of the building. The selected locations and types of sensors are capable of not only capturing the coupled bending‐torsional vibration but also providing an unbiased estimation of the unmeasured responses of seismic‐vulnerable components and bidirectional ground motions. A Kalman filtering algorithm‐based data fusion technique is employed to deal with the measurement data from multi‐type sensors. With the target of achieving the best reconstruction, an optimal sensor placement (OSP) configuration, which minimizes the reconstruction error, is achieved through a sequential sensor placement algorithm. To assess the feasibility of the proposed OSP framework and the accuracy of reconstructed responses and ground motions, a numerical study is also performed on the full‐scale 3D FE model of a super high‐rise building. The torsional effects of the building under bidirectional long‐period ground motions are assessed by comparison with the results without considering the torsional effect. The numerical results show that the proposed framework is feasible and the reconstructed responses and ground motions are accurate and that the torsional effects cannot be neglected in the seismic assessment of the building.

show abstract

A streamline approach to multiaxial fatigue monitoring using virtual sensing

Terrazas

Sedehi

Papadimitriou

et al. 2021

Structural Contr & Hlth

View full text Add to dashboard Cite

This paper presents a framework for fatigue monitoring on linear components of a structure subjected to unknown loads using a limited number of outputonly vibration measurements. It exploits virtual sensing techniques to estimate the dynamical responses, including virtual multiaxial strain/stress time histories. It applies joint input-state estimation algorithms to predict the dynamical response of the structural components. The resulting virtual multiaxial stress histories are used to compute the fatigue load cycles and corresponding fatigue damage. The proposed streamline approach allows the use of different available methodologies for virtual sensing and multiaxial fatigue analysis. A numerical validation example is provided using the tower of a reference wind turbine model subject to realistic wind loads. The proposed procedure can be adopted over a monitoring period to produce a global fatigue damage accumulation map from limited vibration data. It thus provides realistic fatigue damage accumulation information for a component based on the actual operational conditions of a structure.

show abstract

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

Cited by 90 publications

References 25 publications

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

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

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

A streamline approach to multiaxial fatigue monitoring using virtual sensing

Contact Info

Product

Resources

About