Damage Detection Based on Model Updating Methods

Fritzen, Claus‐Peter; Jennewein, D.; Kiefer, T.

doi:10.1006/mssp.1997.0139

Cited by 206 publications

(116 citation statements)

References 0 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…Thus, the residuals I 2 ðyÞ are exactly the same as alternative measures of fit based on MAC values defined in the literature [3].…”

Section: Modal Residualsmentioning

confidence: 91%

“…For the second group, the measure of fit I 2 ðyÞ is selected to represent the difference between the measured and the model predicted mode shape components for all modes. Specifically, the two measures of fit are given by Alternative measures of fit [3,[19][20][21][22] can easily be accommodated in the proposed formulation. In particular, it is straightforward to show that…”

Section: Modal Residualsmentioning

confidence: 99%

See 1 more Smart Citation

Variability of updated finite element models and their predictions consistent with vibration measurements

Papadimitriou¹,

Ntotsios²,

Giagopoulos³

et al. 2011

Struct. Control Health Monit.

View full text Add to dashboard Cite

Abstract:A case study on a small-scale laboratory vehicle frame is used to investigate the variability of the updated finite element (FE) models that arises from model and measurement errors and demonstrate the effect of this variability on response predictions. Conventional weighted modal residuals and recently introduced multi-objective identification methods for structural model updating are used to provide the entire spectrum of Pareto optimal FE models consistent with the measured modal data. Similarities and differences between the two model updating methods are explored and the advantages of the multi-objective identification methods are emphasized. A significant variability in Pareto optimal models is observed, which is indicative of the uncertainty in the updated FE models. The dependence of the variability of the Pareto models on the information contained in the measured data and the size of model and measurement errors is explored by varying the number of measured modes, number of sensors, FE mesh discretization sizes, and number of model parameters. The effectiveness of the updated Pareto optimal models and their predictive capabilities are assessed. Frequency response functions and fatigue lifetime predictions are used as example of structural performance variables in order to demonstrate the variability in the response predictions that arises from the variability in the Pareto optimal models. A large variability in the response predictions is observed that cannot be ignored in decisions based on updated FE models. The multi-objective optimization method provides the general framework for properly accounting for model uncertainty in model-based response predictions consistent with measured data.

show abstract

“…Thus, the residuals I 2 ðyÞ are exactly the same as alternative measures of fit based on MAC values defined in the literature [3].…”

Section: Modal Residualsmentioning

confidence: 91%

Section: Modal Residualsmentioning

confidence: 99%

Variability of updated finite element models and their predictions consistent with vibration measurements

Papadimitriou¹,

Ntotsios²,

Giagopoulos³

et al. 2011

Struct. Control Health Monit.

View full text Add to dashboard Cite

show abstract

“…Specifically, the two measures of fit are given by experimentally identified and estimated mode shapes for the r -th mode. Alternative measures of fit can easily be used and found in literature [18][19][20][21][22].…”

Section: Modal Residualsmentioning

confidence: 99%

Parameter Estimation of Nonlinear Large Scale Systems Through Stochastic Methods and Measurement of Its Dynamic Response

Giagopoulos¹,

Αραϊλόπουλος²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

View full text Add to dashboard Cite

show abstract

“…In the past few decades, various kinds of FE model updating approaches have been widely investigated based on the actually observed behaviors of the system. Additionally, experimental modal and vibration data are often used in FE model updating in the field of structural dynamics [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Chen

Shen

Liu

2018

Shock and Vibration

View full text Add to dashboard Cite

As the uncertainty is widely existent in the engineering structure, it is necessary to study the finite element (FE) modeling and updating in consideration of the uncertainty. A FE model updating approach in structural dynamics with interval uncertain parameters is proposed in this work. Firstly, the mathematical relationship between the updating parameters and the output interesting qualities is created based on the copula approach and the vast samples of inputs and outputs are obtained by the Monte Carlo (MC) sampling technology according to the copula model. Secondly, the samples of updating parameters are rechosen by combining the copula model and the experiment intervals of the interesting qualities. Next, 95% confidence intervals of updating parameters are calculated by the nonparameter kernel density estimation (KDE) approach, which is regarded as the intervals of updating parameters. Lastly, the proposed approach is validated in a two degree-of-freedom mass-spring system, simple plates, and the transport mirror system. The updating results evidently demonstrate the feasibility and reliability of this approach.

show abstract

Damage Detection Based on Model Updating Methods

Cited by 206 publications

References 0 publications

Variability of updated finite element models and their predictions consistent with vibration measurements

Variability of updated finite element models and their predictions consistent with vibration measurements

Parameter Estimation of Nonlinear Large Scale Systems Through Stochastic Methods and Measurement of Its Dynamic Response

A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Contact Info

Product

Resources

About