Bayesian parameter estimation of Euler-Bernoulli beams

Abstract

This paper develops a statistical signal processing algorithm for parameter estimation of Euler-Bernoulli beams from limited and noisy measurement. The original problem is split into two reduced-order sub-problems coupled by a linear equation. The first sub-problem is cast as an inverse problem and solved by using Bayesian approximation error analysis. The second sub-problem is cast as a forward problem and solved by using the finite element technique. An optimal solution to the original problem is then obtained by coupling the solutions to the two sub-problems. Finally, a statistical signal processing algorithm for adaptive estimation of the optimal solution is developed. Computer simulation shows the effectiveness of the proposed algorithm.