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Proceedings 2017, 1(2), 41; doi:10.3390/ecsa-3-D006

Optimal Sensor Placement through Bayesian Experimental Design: Effect of Measurement Noise and Number of Sensors

Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. da Vinci 32, 20133 Milan, Italy
ETH Zürich, Institute of Structural Engineering, Stefano-Franscini-Platz, 5, 8093 Zürich, Switzerland
Presented at the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Available online:
Author to whom correspondence should be addressed.
Published: 14 November 2016
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Sensors networks for the health monitoring of structural systems ought to be designed to render both accurate estimations of the relevant mechanical parameters and an affordable experimental setup. Therefore, the number, type and location of the sensors have to be chosen so that the uncertainties related to the estimated health are minimized. Several deterministic methods based on the sensitivity of measures with respect to the parameters to be tuned are widely used. Despite their low computational cost, these methods do not take into account the uncertainties related to the measurement process. In former studies, a method based on the maximization of the information associated with the available measurements has been proposed and the use of approximate solutions has been extensively discussed. Here we propose a robust numerical procedure to solve the optimization problem: in order to reduce the computational cost of the overall procedure, Polynomial Chaos Expansion and a stochastic optimization method are employed. The method is applied to a flexible plate. First of all, we investigate how the information changes with the number of sensors; then we analyze the effect of choosing different types of sensors (with their relevant accuracy) on the information provided by the structural health monitoring system.
Keywords: optimal sensor placement; bayes; structural health monitoring; experimental design optimal sensor placement; bayes; structural health monitoring; experimental design
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Capellari, G.; Chatzi, E.; Mariani, S. Optimal Sensor Placement through Bayesian Experimental Design: Effect of Measurement Noise and Number of Sensors. Proceedings 2017, 1, 41.

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