Optimal sensor placement methodology for parametric identification of structural systems

Papadimitriou, Costas

doi:10.1016/j.jsv.2003.10.063

Cited by 378 publications

(352 citation statements)

References 23 publications

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“…The posterior distribution (6) is derived by some manipulation, resulting also in a Gaussian distribution: (13) where c1 and c2 are constants and the posterior covariance…”

Section: Empirical Bayesian Virtual Sensingmentioning

confidence: 99%

“…They present the most commonly applied algorithms and criteria. The sensor placement is a discrete optimization problem, for which genetic algorithms have been proposed [12][13][14]. Alternatively, a computationally efficient and widely used algorithm is to start with a large set of candidate sensor locations and removing one sensor in each round based on the selected criterion until the selected number of sensors remains.…”

Section: Introductionmentioning

confidence: 99%

“…Another iterative method is to start with a small number of sensors and add one sensor in each round to the sensor network until the required criterion is fulfilled. The algorithm is called forward sequential sensor placement (FSSP) algorithm [13,17,18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Obtaining High Accuracy Vibration Measurements With Low-Cost Sensors Using Bayesian Virtual Sensing

Kullaa¹

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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“…The posterior distribution (6) is derived by some manipulation, resulting also in a Gaussian distribution: (13) where c1 and c2 are constants and the posterior covariance…”

Section: Empirical Bayesian Virtual Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Obtaining High Accuracy Vibration Measurements With Low-Cost Sensors Using Bayesian Virtual Sensing

Kullaa¹

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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“…The main goal of the present work is to determine the optimal configuration of sensors, that guarantees the best estimation of mechanical parameters or, in other words, the maximum amount of inferred information, under noise corrupted variables. The general purpose framework proposed in [6] is applied to the OSP problem, in order to find the sensor configuration guaranteeing a maximal amount of information, as already proposed in [7] and [8]. The remainders of the paper is organized as follows: in Section 2, the main theoretical aspects of the adopted methodology, such as the problem settings, the numerical solution of the problem and the optimization scheme, are described.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Sensor Placement Method for SHM Based on Bayesian Experimental Design and Polynomial Chaos Expansion

Capellari¹,

Chatzi²,

Mariani³

2016

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

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Abstract. We present an optimal sensor placement methodology for structural health monitoring (SHM) purposes, relying on a Bayesian experimental design approach. The unknown structural properties, e.g. the residual strength and stiffness, are inferred from data collected through a network of sensors, whose architecture, i.e., type and position may largely affect the accuracy of the monitoring system. In tackling this issue, an optimal network configuration is herein sought by maximizing the expected information gain between prior and posterior probability distributions of the parameters to be estimated. Since the objective function linked to the network topology cannot be analytically computed, a numerical approximation is provided by means of a Monte Carlo analysis, wherein each realization is obtained via finite element modeling. Since the computational burden linked to this procedure often grows infeasible, a Polynomial Chaos Expansion (PCE) approach is adopted for accelerating the computation of the forward problem. The analysis expands over joint samples covering both structural state and design variables, i.e., sensor locations. Via increase of the number of deployed sensors in the network, the optimization procedure soon turns computationally costly due to the curse of dimensionality. To this end, a stochastic optimization method is adopted for accelerating the convergence of the optimization process and thereby the damage detection capability of the SHM system. The proposed method is applied to thin flexible structures, and the resulting optimal sensor configuration is shown. The effects of the number of training samples, the polynomial degree of the approximation expansion and the optimization settings are also discussed.

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“…Genetic algorithms have been successfully used for error minimization in various damage identification problems [2,[4][5][6][7][8] and have gained wide acceptance due to their inherent advantages such as convergence to global optima, noise tolerance, handling of multimodal problems (non-unique solutions), and gradient-free search.…”

Section: Introductionmentioning

confidence: 99%

Co‐evolutionary algorithm for structural damage identification using minimal physical testing

Kouchmeshky

Aquino

Bongard

et al. 2006

Numerical Meth Engineering

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SUMMARYThe problem of damage identification using minimum test data is studied in this work. Data sparsity in damage identification applications commonly results in inverse problems that are mathematically ill-posed (e.g. non-unique solutions). Although solution non-uniqueness may be addressed by performing multiple tests on a structure, it is not trivial to decide which tests to carry out given that actual physical testing is costly. This problem is addressed in this work through a new co-evolutionary algorithm that interactively searches for damage scenarios and optimum physical tests. The algorithm is composed of two stages: the estimation phase, which searches for damage scenarios that can predict current physical tests, and the exploration phase, which searches for tests that increase the level of information about the damaged system. The feasibility of the methodology is demonstrated using numerical examples.

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Optimal sensor placement methodology for parametric identification of structural systems

Cited by 378 publications

References 23 publications

Obtaining High Accuracy Vibration Measurements With Low-Cost Sensors Using Bayesian Virtual Sensing

Obtaining High Accuracy Vibration Measurements With Low-Cost Sensors Using Bayesian Virtual Sensing

An Optimal Sensor Placement Method for SHM Based on Bayesian Experimental Design and Polynomial Chaos Expansion

Co‐evolutionary algorithm for structural damage identification using minimal physical testing

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