88,140 research outputs found
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
This paper develops mfEGRA, a multifidelity active learning method using
data-driven adaptively refined surrogates for failure boundary location in
reliability analysis. This work addresses the issue of prohibitive cost of
reliability analysis using Monte Carlo sampling for expensive-to-evaluate
high-fidelity models by using cheaper-to-evaluate approximations of the
high-fidelity model. The method builds on the Efficient Global Reliability
Analysis (EGRA) method, which is a surrogate-based method that uses adaptive
sampling for refining Gaussian process surrogates for failure boundary location
using a single-fidelity model. Our method introduces a two-stage adaptive
sampling criterion that uses a multifidelity Gaussian process surrogate to
leverage multiple information sources with different fidelities. The method
combines expected feasibility criterion from EGRA with one-step lookahead
information gain to refine the surrogate around the failure boundary. The
computational savings from mfEGRA depends on the discrepancy between the
different models, and the relative cost of evaluating the different models as
compared to the high-fidelity model. We show that accurate estimation of
reliability using mfEGRA leads to computational savings of 46% for an
analytic multimodal test problem and 24% for a three-dimensional acoustic horn
problem, when compared to single-fidelity EGRA. We also show the effect of
using a priori drawn Monte Carlo samples in the implementation for the acoustic
horn problem, where mfEGRA leads to computational savings of 45% for the
three-dimensional case and 48% for a rarer event four-dimensional case as
compared to single-fidelity EGRA
A non-Gaussian continuous state space model for asset degradation
The degradation model plays an essential role in asset life prediction and condition based maintenance. Various degradation models have been proposed. Within these models, the state space model has the ability to combine degradation data and failure event data. The state space model is also an effective approach to deal with the multiple observations and missing data issues. Using the state space degradation model, the deterioration process of assets is presented by a system state process which can be revealed by a sequence of observations. Current research largely assumes that the underlying system development process is discrete in time or states. Although some models have been developed to consider continuous time and space, these state space models are based on the Wiener process with the Gaussian assumption. This paper proposes a Gamma-based state space degradation model in order to remove the Gaussian assumption. Both condition monitoring observations and failure events are considered in the model so as to improve the accuracy of asset life prediction. A simulation study is carried out to illustrate the application procedure of the proposed model
Meta-models for structural reliability and uncertainty quantification
A meta-model (or a surrogate model) is the modern name for what was
traditionally called a response surface. It is intended to mimic the behaviour
of a computational model M (e.g. a finite element model in mechanics) while
being inexpensive to evaluate, in contrast to the original model which may take
hours or even days of computer processing time. In this paper various types of
meta-models that have been used in the last decade in the context of structural
reliability are reviewed. More specifically classical polynomial response
surfaces, polynomial chaos expansions and kriging are addressed. It is shown
how the need for error estimates and adaptivity in their construction has
brought this type of approaches to a high level of efficiency. A new technique
that solves the problem of the potential biasedness in the estimation of a
probability of failure through the use of meta-models is finally presented.Comment: Keynote lecture Fifth Asian-Pacific Symposium on Structural
Reliability and its Applications (5th APSSRA) May 2012, Singapor
- …