125,343 research outputs found
Calibration and improved prediction of computer models by universal Kriging
This paper addresses the use of experimental data for calibrating a computer
model and improving its predictions of the underlying physical system. A global
statistical approach is proposed in which the bias between the computer model
and the physical system is modeled as a realization of a Gaussian process. The
application of classical statistical inference to this statistical model yields
a rigorous method for calibrating the computer model and for adding to its
predictions a statistical correction based on experimental data. This
statistical correction can substantially improve the calibrated computer model
for predicting the physical system on new experimental conditions. Furthermore,
a quantification of the uncertainty of this prediction is provided. Physical
expertise on the calibration parameters can also be taken into account in a
Bayesian framework. Finally, the method is applied to the thermal-hydraulic
code FLICA 4, in a single phase friction model framework. It allows to improve
the predictions of the thermal-hydraulic code FLICA 4 significantly
Degradation modeling applied to residual lifetime prediction using functional data analysis
Sensor-based degradation signals measure the accumulation of damage of an
engineering system using sensor technology. Degradation signals can be used to
estimate, for example, the distribution of the remaining life of partially
degraded systems and/or their components. In this paper we present a
nonparametric degradation modeling framework for making inference on the
evolution of degradation signals that are observed sparsely or over short
intervals of times. Furthermore, an empirical Bayes approach is used to update
the stochastic parameters of the degradation model in real-time using training
degradation signals for online monitoring of components operating in the field.
The primary application of this Bayesian framework is updating the residual
lifetime up to a degradation threshold of partially degraded components. We
validate our degradation modeling approach using a real-world crack growth data
set as well as a case study of simulated degradation signals.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS448 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …