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Gaussian process time series model for life prognosis of metallic structures (2009)
| Venue: | Journal of Intelligent Material Systems and Structures |
| Citations: | 4 - 2 self |
BibTeX
@ARTICLE{Mohanty09gaussianprocess,
author = {Subhasish Mohanty and Santanu Das and Aditi Chattopadhyay and Pedro Peralta},
title = {Gaussian process time series model for life prognosis of metallic structures},
journal = {Journal of Intelligent Material Systems and Structures},
year = {2009},
pages = {887--896}
}
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Abstract
ABSTRACT: Al 2024-T351 has been modeled using a kernel-based multi-variate Gaussian Process approach. The Gaussian Process model projects fatigue affecting input variables to output crack growth by probabilistically inferring the underlying nonlinear relationship between input and output. The Gaussian Process approach not only explicitly models the uncertainty due to scatter in material microstructure parameter, but it also implicitly models the loading sequence effect due to variable loading. The loading sequence effect is modeled through the Gaussian Process optimal hyperparameters by using the crack length data observed over the entire domain of spectrum loading. The performance in the crack growth prediction is evaluated for two covariance functions, a radial basis based, anisotropic covariance function and a neural network-based isotropic covariance function. Furthermore, the performance of different types of scaling, used to scale the input–output data space, is tested. It is found that for the radial basis based anisotropic covariance function with normalized scaling, the prediction error is consistently lower compared to other combinations. In addition, the Gaussian Process model allows determination of the collapse load condition, which is a desirable feature for on-line health monitoring and prognosis. Key Words: prognosis, fatigue crack growth, 2024-T351 aluminum alloy, variable loading, Gaussian Process, covariance function, maximum likelihood optimization, hyperparameters.
Keyphrases
gaussian process time series model covariance function metallic structure life prognosis variable loading radial basis neural network-based isotropic covariance function gaussian process kernel-based multi-variate gaussian process approach loading sequence effect crack growth prediction gaussian process approach crack length data gaussian process model project input variable sequence effect entire domain desirable feature 2024-t351 aluminum alloy input output data space spectrum loading material microstructure parameter nonlinear relationship normalized scaling output crack growth gaussian process optimal hyperparameters gaussian process model anisotropic covariance function different type maximum likelihood optimization on-line health monitoring key word collapse load condition prediction error fatigue crack growth
