Optimal instrumentation of structures on flexible base for system identification

Heredia‐Zavoni, Ernesto; Montes-Iturrizaga, R.; Esteva, Luis

doi:10.1002/(sici)1096-9845(199912)28:12<1471::aid-eqe872>3.0.co;2-m

Cited by 34 publications

(16 citation statements)

References 6 publications

(1 reference statement)

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“…In the past, statistical-based approaches (e.g., [22][23][24][25][26][27][28][29][30]) have been developed to provide rational solutions to the optimal sensor location problem. In the past, statistical-based approaches (e.g., [22][23][24][25][26][27][28][29][30]) have been developed to provide rational solutions to the optimal sensor location problem.…”

Section: -2mentioning

confidence: 99%

“…Moreover, a model updating methodology is useful in predicting the structural damage by continually updating the structural model using vibration data [4][5][6][7][8][9][10][11][12]. In the past, statistical-based approaches (e.g., [22][23][24][25][26][27][28][29][30]) have been developed to provide rational solutions to the optimal sensor location problem. The updated information about the condition of the structure can be used to identify potentially unsafe structures, to schedule inspection intervals, repairs or maintenance, or to design retrofitting or control strategies for structures that are found to be vulnerable to possible future loads.…”

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confidence: 99%

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Bayesian Modeling and Updating

Katafygiotis¹,

Papadimitriou²

2004

Engineering Design Reliability Handbook

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The problem of structural model updating has gained much interest as finite element model capabilities and modal testing have become more mature areas of structural dynamics (e.g., [1][2][3]). The need for model updating arises because there are always errors associated with the process of constructing a theoretical model of a structure, and this leads to uncertain accuracy in predictive response. Moreover, a model updating methodology is useful in predicting the structural damage by continually updating the structural model using vibration data [4][5][6][7][8][9][10][11][12]. Such updated models obtained periodically throughout the lifetime of the structure can be further used to update the response predictions and lifetime structural reliability based on available data [13][14][15][16]. The updated information about the condition of the structure can be used to identify potentially unsafe structures, to schedule inspection intervals, repairs or maintenance, or to design retrofitting or control strategies for structures that are found to be vulnerable to possible future loads.Structural model updating is an inverse problem according to which a model of a structure, usually a finite element model, is adjusted so that either the calculated time histories, frequency response functions, or modal parameters best match the corresponding quantities measured or identified from the test data. This inverse process aims at providing updated models and their corresponding uncertainties based on the data. These updated models are expected to give more accurate response predictions to future loadings, as well as allow for an estimation of the uncertainties associated with such response predictions. In practice, the inverse problem of model updating is usually ill-conditioned due to insensitivity of the response to changes in the model parameters, and nonunique [17-20] because of insufficient available data relative to -2Engineering Design Reliability Handbook the desired model complexity. Additional difficulties associated with the development of an effective model updating methodology include: (1) model error present due to the fact that the chosen class of structural models is unable to exactly model the actual behavior of the structure; (2) measurement noise in the dynamic data, especially for higher modes; (3) incomplete set of observed DOFs (degrees of freedom) due to the limited number of sensors available and the limited accessibility throughout the structure; (4) incomplete number of contributing modes due to limited bandwidth in the input and the dynamic response.This chapter presents a Bayesian framework for statistical modeling and updating of structural models utilizing measured dynamic data, along with its use in making informed structural response predictions and reliability assessments. The Bayesian approach to statistical modeling uses probability as a way of quantifying the plausibilities associated with the various models and the parameters of these models given the observed data. The Bayes rule uses a prior...

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Section: -2mentioning

confidence: 99%

mentioning

confidence: 99%

Bayesian Modeling and Updating

Katafygiotis¹,

Papadimitriou²

2004

Engineering Design Reliability Handbook

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“…The approaches stand out from one each other according to the optimal criterion used to select the best placements. Notable ones are: norms of the Fisher information matrix [1] - [2], expected Bayesian loss function [3], information entropy index [4] - [5] . The issue at hand has also been tackled in other acoustic fields: modal identification [6] and design of spherical microphone arrays [7] - [8].…”

Section: Introductionmentioning

confidence: 99%

Iterative positioning of microphone arrays for acoustic imaging

Gilquin

Lecomte

Antoni

et al. 2020

Journal of Sound and Vibration

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For the characterization of acoustic sources, a common approach is to retropropagate the sound pressure measured with a microphone array, which is often performed through the resolution of an inverse problem. The ill-posed nature of this problem, as well as the limited number of measurements, are known to reduce the quality of the source reconstruction. A practical solution to these limitations is to increase the number of measurements with new array placements. However, finding the best array positions is not a straightforward process. The present paper tackles this issue by introducing a sequential approach that select at each iteration the optimal array placement. The proposed approach builds on two features rooted in a Bayesian framework: an inverse method called "Bayesian focusing" and a Bayesian search criterion based on the Kullback-Leibler divergence. Simulations results for the characterization of a directive source are used to illustrate the performance of the proposed approach. It is shown that for a fixed number of iterations, the proposed approach performs better than ones where the successive placements are randomly selected around the source, or others where the placements follow a deterministic spherical grid pattern.

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“…The optimal sensor configuration is selected as the one that maximizes some norm (determinant or trace) of the Fisher information matrix (FIM). In other studies,() the optimal sensor configuration has been chosen as the one that minimizes the expected Bayesian loss function involving the trace of the inverse of the FIM. A Bayesian framework to optimal sensor location for structural health monitoring has also been introduced in Flynn and Todd.…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimal sensor placement for crack identification in structures using strain measurements

Argyris

Chowdhury

Zabel

et al. 2018

Struct Control Health Monit

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A Bayesian framework is presented for finding the optimal locations of strain sensors in a plate with a crack with the goal of identifying the crack properties, such as crack location, size, and orientation. Sensor grids of different type and size are considered. The Bayesian optimal sensor placement framework is rooted in information theory, and the optimal grid is the one which maximizes the expected information gain (Kullback-Liebler divergence) between the prior and posterior probability density functions of the crack parameters. The uncertainty in the crack parameters is accounted for naturally within the Bayesian framework through the prior probability density functions. The framework is demonstrated for a thin plate with crack, subjected to static loading. A finite element model is used to simulate the strain distributions in the plate given the crack properties. To verify the effectiveness of the proposed optimal sensor placement methodology, the estimated optimal sensor grids are used to perform Bayesian crack identification using simulated data. Parametric analyses are carried out giving emphasis on the effect of the number of sensors, grid type, and experimental data noise levels in the identification results. KEYWORDSbayesian inference, crack identification, information gain, KL-divergence, optimal sensor placement Struct Control Health Monit. 2018;25:e2137.wileyonlinelibrary.com/journal/stc

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Optimal instrumentation of structures on flexible base for system identification

Cited by 34 publications

References 6 publications

Bayesian Modeling and Updating

Bayesian Modeling and Updating

Iterative positioning of microphone arrays for acoustic imaging

Bayesian optimal sensor placement for crack identification in structures using strain measurements

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