Comparison of Different Model Classes for Bayesian Updating and Robust Predictions using Stochastic State-Space System Models
2009
A stochastic system-based framework for Bayesian
model updating of dynamic systems was presented
in Beck and Katafygiotis (1998). One key concept
in this framework is a stochastic system model class
which consists of probabilistic predictive input-output
models for a system together with a prior probability
distribution over this set that quantifies the initial
relative plausibility of each predictive model. Past
applications of this framework focus on model classes
which consider an uncertain prediction error as the difference
between the real system output and the model
output and model it probabilistically using Jaynes'
Principle of Maximum Information Entropy.
In this paper, in addition to these model classes,
we also consider an extension of such model classes
to allow more flexibility in treating modeling uncertainties
when updating state space models and making
robust predictions; this is done by introducing prediction
errors in the state vector equation in addition
to those in the system output vector equation. The
extended model classes allow for interactions between
the model parameters and the prediction errors in both
the state vector equation and the system output equation
to give more robust predictions at unobserved
DOFs. Bayesian model class selection is used to evaluate
the posterior probability of model classes for the
comparison of the extended model classes and the
original one. To make predictions robust to model
uncertainties, Bayesian model averaging is used to
combine the predictions of these model classes. State-of-
the-art algorithms (Cheung & Beck 2007, 2008;
Ching & Chen 2007) are used to solve the computational
problems involved. The importance and
effectiveness of the proposed method is illustrated with
examples for robust reliability updating of structural
systems.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
7
Citations
NaN
KQI