An optimal sensor location methodology for designing modal experiments

Abstract

A methodology is proposed for making decisions regarding the optimal location of sensors for modal identification. Uncertainties are quantified using probability distributions, and a Bayesian methodology is proposed for deriving appropriate expressions for the updated probability density function (PDF) of the modal parameters based on measured ambient response time histories. The optimal sensor configuration is selected as the one that minimizes the information entropy which is a unique measure of the uncertainty in the modal parameters. The information entropy measure is also extended to handle large uncertainties expected in the pre-test nominal modal model of a structure. Genetic algorithms are well-suited for solving the resulting discrete optimization problem. In experimental design, the proposed information entropy can be used to design cost-effective modal experiments by exploring, comparing and evaluating the benefits from placing additional sensors in the structure in relation to the improvement in the quality of the modal predictions