BAYESIAN OPTIMAL EXPERIMENTAL DESIGN INVOLVING MULTIPLE SETUPS FOR DYNAMIC STRUCTURAL TESTING

摘要

In an experimental design for dynamic structural testing, it is a common practice to obtain data from a structure using multiple setups, with each setup covering a different part of the structure. This is generally due to availability of limited number of sensors with synchronous data acquisition. This paper considers the problem of optimal placement of actuators and sensors with the aim of maximizing the data information in an experimental design with multiple setups so that the structural dynamic behavior can be fully characterized. The uncertainty in the model parameters is computed by a Bayesian statistical framework and expected utility is used to quantify the uncertainty of the set of identified model parameters. The problem of optimal experimental design with multiple setups is formulated as an optimization problem in which the actuators' and sensors' configuration, which maximizes the expected utility, is selected as the optimal one. The proposed approach can be used to compare and evaluate the quality of the parameter estimates that can be achieved with different numbers of setups in an experimental design and a different number of actuators and sensors in each setup. The effectiveness of the proposed approach is illustrated by optimal experimental design for a simple 12-degree-of-freedom (dof) chainlike spring-mass model of a structure and a 37-dof truss structure.