A hierarchical Bayesian method for vibration-based time domain force reconstruction problems

A hierarchical Bayesian method for vibration-based time domain force reconstruction problems Traditional force reconstruction techniques require prior knowledge on the force nature to determine the regularization term. When such information is unavailable, the inappropriate term is easily chosen and the reconstruction result becomes unsatisfactory. In this paper, we propose a novel method to automatically determine the appropriate q as in ℓq regularization and reconstruct the force history. The method incorporates all to-be-determined variables such as the force history, precision parameters and q into a hierarchical Bayesian formulation. The posterior distributions of variables are evaluated by a Metropolis-within-Gibbs sampler. The point estimates of variables and their uncertainties are given. Simulations of a cantilever beam and a space truss under various loading conditions validate the proposed method in providing adaptive determination of q and better reconstruction performance than existing Bayesian methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Sound and Vibration Elsevier

A hierarchical Bayesian method for vibration-based time domain force reconstruction problems

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0022-460X
eISSN
1095-8568
D.O.I.
10.1016/j.jsv.2018.01.052
Publisher site
See Article on Publisher Site

Abstract

Traditional force reconstruction techniques require prior knowledge on the force nature to determine the regularization term. When such information is unavailable, the inappropriate term is easily chosen and the reconstruction result becomes unsatisfactory. In this paper, we propose a novel method to automatically determine the appropriate q as in ℓq regularization and reconstruct the force history. The method incorporates all to-be-determined variables such as the force history, precision parameters and q into a hierarchical Bayesian formulation. The posterior distributions of variables are evaluated by a Metropolis-within-Gibbs sampler. The point estimates of variables and their uncertainties are given. Simulations of a cantilever beam and a space truss under various loading conditions validate the proposed method in providing adaptive determination of q and better reconstruction performance than existing Bayesian methods.

Journal

Journal of Sound and VibrationElsevier

Published: May 12, 2018

References

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