On the optimal location of sensors for parametric identification of linear structural systems

Kirkegaard, Poul Henning; Brincker, Rune

doi:10.1006/mssp.1994.1045

Cited by 73 publications

(33 citation statements)

References 4 publications

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“…In particular, information theory based approaches (e.g. Kammer, 1991;Kirkegaard and Brincker, 1994;Udwadia, 1994;Papadimitriou et al, 2000) have been developed to provide rational solutions to several issues encountered in the problem of selecting the optimal sensor configuration. These approaches are closely correlated with the problem of identification and damage detection using vibrational or modal properties.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian methodology for crack identification in structures using strain measurements

Gaitanaros

Karaiskos

Papadimitriou

et al. 2010

IJRS

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A Bayesian system identification methodology is presented for estimating the crack location, size and orientation in a structure using strain measurements. The Bayesian statistical approach combines information from measured data and analytical or computational models of structural behaviour to predict estimates of the crack characteristics along with the associated uncertainties, taking into account modelling and measurement errors. An optimal sensor location methodology is also proposed to maximise the information that is contained in the measured data for crack identification problems. For this, the most informative, about the condition of the structure, data are obtained by minimising the information entropy measure of the uncertainty in the crack parameter estimates. Both crack identification and optimal sensor location formulations lead to highly non-convex optimisation problems in which multiple local and global optima may exist. A hybrid optimisation method, based on evolutionary strategies and gradient-based techniques, is used to determine the global minima. The effectiveness of the proposed methodologies is illustrated using simulated data from a single crack in a thin plate subjected to static loading.

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Section: Introductionmentioning

confidence: 99%

A Bayesian methodology for crack identification in structures using strain measurements

Gaitanaros

Karaiskos

Papadimitriou

et al. 2010

IJRS

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“…As a general trend (see for instance [7][8][9][10]), the sensor selection problem is addressed in the framework of information theory. Metrics based on the observability Grammian or on the Fisher information matrix are defined in order to optimise the configuration of the experiment.…”

Section: Introductionmentioning

confidence: 99%

The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring

Borguet

Léonard

2008

International Journal of Rotating Machinery

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Engine health monitoring has been an area of intensive research for many years. Numerous methods have been developed with the goal of determining a faithful picture of the engine condition. On the other hand, the issue of sensor selection allowing an efficient diagnosis has received less attention from the community. The present contribution revisits the problem of sensor selection for engine performance monitoring within the scope of information theory. To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix. An example application consisting in a commercial turbofan engine illustrates the enhancement that can be expected from a wise selection of the sensor set.

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“…Shah & Udwadia 1978, Kammer 1991, Kirkegaard & Brincker 1994 have been developed to provide rational solutions to several issues encountered in the problem of selecting the optimal sensor configuration. The optimal sensor configuration is taken as the one that maximizes some norm (determinant or trace) of the Fisher information matrix (FIM).…”

Section: Introductionmentioning

confidence: 99%

Optimal experimental design for structural health monitoring applications

Lam¹,

Papadimitriou²,

Ntotsios³

2008

World Forum on Smart Materials and Smart Structures Technology

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Successful structural health monitoring and condition assessment depends to a large extent on the sensor and actuator networks place on the structure as well as the excitation characteristics. An optimal experimental design methodology deals with the issue of optimizing the sensor and actuator network, as well as the excitation characteristics, such that the resulting measured data are most informative for monitoring the condition of the structure. Theoretical and computational issues arising in optimal experimental design are addressed. The problem is formulated as a multi-objective optimization problem of finding the Pareto optimal sensor configurations that simultaneously minimize appropriately defined information entropy indices related to monitoring multiple probable damage scenarios. Asymptotic estimates for the information entropy, valid for large number of measured data, are used to rigorously justify that the selection of the optimal experimental design can be based solely on nominal structural models associated with the probable damage scenarios, ignoring the details of the measured data that are not available in the experimental design stage. Heuristic algorithms are proposed for constructing effective, in terms of accuracy and computational efficiency, sensor configurations. The effectiveness of the proposed method is illustrated by designing the optimal sensor configurations for monitoring damage on a shear model of a building structure.

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On the optimal location of sensors for parametric identification of linear structural systems

Cited by 73 publications

References 4 publications

A Bayesian methodology for crack identification in structures using strain measurements

A Bayesian methodology for crack identification in structures using strain measurements

The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring

Optimal experimental design for structural health monitoring applications

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