Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering

Papadimitriou, Costas; Fritzen, Claus‐Peter; Kraemer, Peter; Ntotsios, Evangelos

doi:10.1002/stc.395

Cited by 152 publications

(95 citation statements)

References 25 publications

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“…Frequency-domain methods are used in different fields, such as structural health monitoring [15], the FEM environment [16], multi-axial fatigue [17] and a non-normal process fatigue evaluation with the use of a correction factor [18].…”

Section: Introductionmentioning

confidence: 99%

Frequency-domain methods for a vibration-fatigue-life estimation – Application to real data

Mršnik

Slavič

Boltežar

2013

International Journal of Fatigue

234

104

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Section: Introductionmentioning

confidence: 99%

Frequency-domain methods for a vibration-fatigue-life estimation – Application to real data

Mršnik

Slavič

Boltežar

2013

International Journal of Fatigue

234

104

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“…Conventionally, fatigue life predictions are conducted on the basis of numerical simulations in conjunction with the information provided from historical metocean data. In the wake of recent advancements in Structural Health Monitoring technologies and methodologies, significant attention has been redirected to vibration-based approaches for fatigue estimation [1], particularly to what pertains to response prediction under unknown inputs.…”

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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“…Additionally, several state estimation algorithms have been proposed for linear as well as non-linear systems [9,10]. A common approach in state estimation consists of modeling the system input as zero mean Gaussian white noise and applying a Bayesian framework for state estimation [11,12]. In cases where state estimation is performed for uncertain dynamic systems, the system states are estimated together with the unknown system parameters, which is referred to in the literature as joint state and parameter estimation.…”

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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Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering

Cited by 152 publications

References 25 publications

Frequency-domain methods for a vibration-fatigue-life estimation – Application to real data

Frequency-domain methods for a vibration-fatigue-life estimation – Application to real data

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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