Reliability Analysis under Integrated Input Variable and Metamodel Uncertainty Based on Bayesian Approach

2010 ◽  
Author(s):  
An D. ◽  
Choi J. ◽  
Won J.
Keyword(s):  
Reliability Analysis ◽  
Bayesian Approach ◽  
Metamodel Uncertainty

A Predictive Bayesian Approach to Multistate Reliability Analysis

2003 ◽  
Vol 10 (03) ◽  
pp. 221-234 ◽  
Author(s):  
T. Aven ◽  
A. Hjorteland
Keyword(s):  
Reliability Analysis ◽  
Bayesian Approach ◽  
Probability Models ◽  
Bayesian Paradigm ◽  
Bayesian Procedures ◽  
Starting Point ◽  
The World ◽  
The Bayesian Approach

In this paper we discuss how to implement a Bayesian thinking for multistate reliability analysis. The Bayesian paradigm comprises a unified and consistent framework for analysing and expressing reliability, but in our view the standard Bayesian procedures gives too much emphasis on probability models and inference on fictional parameters. We believe that there is a need for a rethinking on how to implement the Bayesian approach, and in this paper we present and discuss such a rethinking for multistate reliability analysis. The starting point of the analysis should be observable quantities, expressing states of the world, not fictional parameters.

Reliability analysis of programmable mechatronics system using Bayesian approach

2009 ◽  
Vol 4 (3) ◽  
pp. 303 ◽  
Author(s):  
R. Amuthakkannan ◽  
S.M. Kannan ◽  
K. Vijayalakshmi ◽  
N. Ramaraj
Keyword(s):  
Reliability Analysis ◽  
Bayesian Approach

Integrating QRA and SRA Methods Within a Bayesian Framework When Calculating Risk in Marine Operations: Two Examples

2000 ◽  
Vol 122 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Wenche K. Rettedal ◽  
Terje Aven ◽  
Ove T. Gudmestad
Keyword(s):  
Risk Analysis ◽  
Reliability Analysis ◽  
Bayesian Approach ◽  
Structural Reliability ◽  
Fault Tree ◽  
Bayesian Approaches ◽  
Objective Risk ◽  
Suitable Framework ◽  
Fully Bayesian ◽  
The Bayesian Approach

This paper concerns itself with the integration of QRA (quantitative risk analysis) and SRA (structural reliability analysis) methods. For simplicity, we will use the term SRA instead of SRA methods in the paper. The Bayesian (subjective) approach seems to be the most appropriate framework for such integrated analyses. It may, however, not be clear to all what the Bayesian approach really means. There exists alternative Bayesian approaches, and the integration of SRA and QRA is very much dependent on what the basis is. The purpose of this paper is to present two marine operation examples, implementing two different Bayesian approaches: the “classical Bayesian approach” and the “fully Bayesian approach.” Following the classical Bayesian approach, we estimate a true, objective risk, whereas in the fully Bayesian approach, risk is a way of expressing uncertainty about future observable quantities. In both examples, one initial accidental event is investigated by using a fault tree and by integrating SRA into this fault tree. We conclude that the most suitable framework for integrating SRA and QRA is to adopt the “fully Bayesian approach.” [S0892-7219(00)00703-2]

The Re-Evaluation of Structural Reliability based on Identification Results

1999 ◽  
Vol 15 (3) ◽  
pp. 109-116
Author(s):  
Pei-Ling Liu ◽  
Yi-Song Chen
Keyword(s):  
Sensitivity Analysis ◽  
System Identification ◽  
Reliability Analysis ◽  
Bayesian Approach ◽  
Structural Reliability ◽  
Service System ◽  
Maintenance Strategy ◽  
Prior Distributions ◽  
The Bayesian Approach

AbstractThis paper develops a method to re-evaluate the reliability of a structure after a period of service. System identification is performed on the structure to identify the current properties of the structure. The Bayesian approach is adopted to modify the prior distributions of the properties based on the identification results. Then, reliability analysis is performed on the structure using the updated distributions of the properties. Sensitivity analysis is also performed to attain the maintenance strategy.

Uncertainty Quantification in Metamodel-Based Reliability Prediction

2016 ◽  
Author(s):  
Saideep Nannapaneni ◽  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Limit State ◽  
Random Variables ◽  
Point Load ◽  
System Model ◽  
Robust Design Optimization ◽  
Statistical Uncertainty ◽  
Input Variables ◽  
Metamodel Uncertainty

Optimization under uncertainty has been studied in two directions — (1) Reliability-based Design Optimization (RBDO), and (2) Robust Design Optimization (RDO). One of the crucial elements in an RBDO problem is reliability analysis. Reliability analysis is affected by different types of epistemic uncertainty, due to inadequate data and modeling errors, along with aleatory uncertainty in input random variables. When the original physics-based model is computationally expensive, a metamodel has often been used in reliability analysis, introducing additional uncertainty due to the metamodel. This work presents a framework to include statistical uncertainty and model uncertainty in metamodel-based reliability analysis. Inadequate data causes uncertainty regarding the statistics (distribution types and distribution parameters) of the input variables, and regarding the system model parameters. Model errors include model form errors, solution approximation errors, and metamodel uncertainty. Two types of metamodels have been considered in literature for reliability analysis: (1) metamodels that compute the system model output over the desired ranges of the input random variables; and (2) metamodels that concentrate only on modeling the limit state. This work focuses on the latter type, using Gaussian process (GP) metamodels for performing both component reliability (single limit state) and system reliability (multiple limit states) analyses. A systematic procedure for the inclusion of model discrepancy terms in the limit-state metamodel construction is developed using an auxiliary variable approach. An efficient single-loop sampling approach using the probability integral transform is used for sampling the input variables with statistical uncertainty. The variability in the GP model prediction (metamodel uncertainty) is also included in reliability analysis through correlated sampling of the model predictions at different inputs. Two mechanical systems — a cantilever beam with point-load at the free end and a two-bar supported panel with point load at its center, are used to demonstrate the proposed techniques.

Lifetime data modelling and reliability analysis based on modified weibull extension distribution and Bayesian approach

2018 ◽  
Vol 32 (11) ◽  
pp. 5121-5126 ◽  
Author(s):  
Yuan-Jian Yang ◽  
Wenhe Wang ◽  
Xin-Yin Zhang ◽  
Ya-Lan Xiong ◽  
Gui-Hua Wang
Keyword(s):  
Reliability Analysis ◽  
Bayesian Approach ◽  
Lifetime Data ◽  
Data Modelling ◽  
Modified Weibull
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