Sensors Location for Updating Problems

Reynier, M.A. Mouret; Abou-Kandil, Hisham

doi:10.1006/mssp.1998.1213

Cited by 45 publications

(25 citation statements)

References 6 publications

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“…There are four major categories of performance criterion: (i) modal parameters based; (ii) response reconstruction based; (iii) energy based; and (iv) information based. The modal parameters based approach compares the modal parameters identified using measurements from different sensor configurations. Commonly used parameters include the modal frequencies and singular values.…”

Section: Introductionmentioning

confidence: 99%

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

Yuen

Kuok

2014

Earthq Engng Struct Dyn

108

104

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SUMMARYIn this paper, a Bayesian sequential sensor placement algorithm, based on the robust information entropy, is proposed for multi-type of sensors. The presented methodology has two salient features. It is a holistic approach such that the overall performance of various types of sensors at different locations is assessed. Therefore, it provides a rational and effective strategy to design the sensor configuration, which optimizes the use of various available resources. This sequential algorithm is very efficient due to its Bayesian nature, in which prior distribution can be incorporated. Therefore, it avoids the possible unidentifiability problem encountered in a sequential process, which starts with small number of sensors. The proposed algorithm is demonstrated using a shear building and a lattice tower with consideration of up to four types of sensors.

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Section: Introductionmentioning

confidence: 99%

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

Yuen

Kuok

2014

Earthq Engng Struct Dyn

108

104

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“…According to sensor placement, identification results can be partially exact as for example with the configuration 2 where only the κ 1 value is correctly identified whereas there is no sensor in the corresponding area. In order to ensure an optimal sensor placement, different approaches can be used, for example, the approach based on observability Grammian , the method using the information entropy , or adjoint‐based approaches . Another way, that we will certainly explore in future works, is to use the spatial PGD modes to localize sensors which probably hold most of the information.…”

Section: Numerical Resultsmentioning

confidence: 99%

Real‐time updating of structural mechanics models using Kalman filtering, modified constitutive relation error, and proper generalized decomposition

Marchand

Chamoin

Rey

2016

Numerical Meth Engineering

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Summary The paper aims at proposing a new strategy for real‐time identification or updating of structural mechanics models defined as dynamical systems. The main idea is to introduce the modified constitutive relation error concept, which is a practical tool that enables to efficiently solve identification problems with highly corrupted data, into the Kalman filtering, which is a classical framework for data assimilation. Furthermore, a PGD‐based model reduction method is performed in order to optimize capabilities of the online updating strategy. Performances of the proposed approach, in terms of robustness gain and computational cost reduction, are illustrated on several unsteady thermal applications. Copyright © 2015 John Wiley & Sons, Ltd.

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“…In [6], Reynier and Abou-Kandil present two methods for sensor selection where one is a gramian based method ensuring observability of the system with the selected sensors. The method allows for independence between the number of selected sensors and modes.…”

Section: Introductionmentioning

confidence: 99%

Redundant Information Rejection in Sensor Localisation Using System Gramians

Gibanica

Abrahamsson

Kammer

2016

Topics in Modal Analysis &Amp; Testing, Volume 10

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Sensors placement is important in vibration testing. The method of effective independence, recently extended to account for triaxial sensors, is widely used for this purpose in case a finite element model of the structure is available. In this paper a criteria is added to reject redundant information that usually arises in symmetric structures or finite element models with high candidate sensor density. A sensor placement strategy is proposed in which, initially, the method of effective independence is used to select the best sensors from a candidate set by maximising the Fisher information matrix determinant. Next, the gramians of a balanced realisation is used to compare the information between systems consisting of only previously added sensors to the final set with systems of the previous and candidate sensors. Sensors with redundant information will have negligible effect on the gramian and can be rejected. The method is fast, as gramians of systems with only one or two outputs are evaluated. It is sub-optimal in the sense that all possible sensor placement combinations are not evaluated for optimality. A test case, consisting of a rectangular plate, is presented, but the method has been used on a large scale industrial model with good results.

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Sensors Location for Updating Problems

Cited by 45 publications

References 6 publications

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

Real‐time updating of structural mechanics models using Kalman filtering, modified constitutive relation error, and proper generalized decomposition

Redundant Information Rejection in Sensor Localisation Using System Gramians

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