Bayesian parameter estimation of Euler-Bernoulli beams

Iman T. Ardekani; Jari Kaipio; Neda Sakhaee; Hamid Sharifzadeh

doi:10.1117/12.2520452

17 April 2019 Bayesian parameter estimation of Euler-Bernoulli beams

Iman T. Ardekani, Jari Kaipio, Neda Sakhaee, Hamid Sharifzadeh

Proceedings Volume 11071, Tenth International Conference on Signal Processing Systems; 110710A (2019) https://doi.org/10.1117/12.2520452
Event: Tenth International Conference on Signal Processing Systems, 2018, Singapore, Singapore

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.

Citation Download Citation

Iman T. Ardekani, Jari Kaipio, Neda Sakhaee, and Hamid Sharifzadeh "Bayesian parameter estimation of Euler-Bernoulli beams", Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710A (17 April 2019); https://doi.org/10.1117/12.2520452

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