Reliability-Based Design Optimization With Confidence Level for Non-Gaussian Distributions Using Bootstrap Method

2010 ◽  
Author(s):  
Yoojeong Noh ◽  
Kyung K. Choi ◽  
Ikjin Lee ◽  
David Gorsich
Keyword(s):  
Standard Deviation ◽  
Statistical Model ◽  
Confidence Intervals ◽  
Confidence Level ◽  
Bootstrap Method ◽  
Reliability Based Design Optimization ◽  
Gaussian Distributions ◽  
Reliability Based Design ◽  
Input Variables ◽  
Non Gaussian

For reliability-based design optimization (RBDO), generating an input statistical model with confidence level has been recently proposed to offset the inaccurate estimation of the input statistical model with Gaussian distributions. For this, the confidence intervals of mean and standard deviation are calculated using the Gaussian distributions of input random variables. However, if the input random variables are non-Gaussian, the use of the Gaussian distributions of input variables will provide inaccurate confidence intervals, and thus, yield undesirable confidence level of the reliability-based optimum design meeting the target reliability βt. In this paper, the RBDO method using the bootstrap method, which does not use the Gaussian distributions of input variables to calculate the confidence intervals of mean and standard deviation, are proposed to obtain the desirable confidence level of output performance for non-Gaussian distributions.

Reliability-Based Design Optimization With Confidence Level for Non-Gaussian Distributions Using Bootstrap Method

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Yoojeong Noh ◽  
Kyung K. Choi ◽  
Ikjin Lee ◽  
David Gorsich ◽  
David Lamb
Keyword(s):  
Statistical Model ◽  
Design Optimization ◽  
Confidence Intervals ◽  
Confidence Level ◽  
Bootstrap Method ◽  
Random Variables ◽  
Reliability Based Design Optimization ◽  
Gaussian Distributions ◽  
Reliability Based Design ◽  
Non Gaussian

For reliability-based design optimization (RBDO), generating an input statistical model with confidence level has been recently proposed to offset inaccurate estimation of the input statistical model with Gaussian distributions. For this, the confidence intervals for the mean and standard deviation are calculated using Gaussian distributions of the input random variables. However, if the input random variables are non-Gaussian, use of Gaussian distributions of the input variables will provide inaccurate confidence intervals, and thus yield an undesirable confidence level of the reliability-based optimum design meeting the target reliability βt. In this paper, an RBDO method using a bootstrap method, which accurately calculates the confidence intervals for the input parameters for non-Gaussian distributions, is proposed to obtain a desirable confidence level of the output performance for non-Gaussian distributions. The proposed method is examined by testing a numerical example and M1A1 Abrams tank roadarm problem.

Reliability-Based Design Optimization With Confidence Level Under Input Model Uncertainty

2009 ◽  
Author(s):  
Yoojeong Noh ◽  
K. K. Choi ◽  
Ikjin Lee ◽  
David Gorsich ◽  
David Lamb
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Design Optimization ◽  
Optimum Design ◽  
Upper Bound ◽  
Confidence Level ◽  
Limited Data ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Input Model

For obtaining correct reliability-based optimum design, an input model needs to be accurately estimated in identification of marginal and joint distribution types and quantification of their parameters. However, in most industrial applications, only limited data on input variables is available due to expensive experimental testing costs. The input model generated from the insufficient data might be inaccurate, which will lead to incorrect optimum design. In this paper, reliability-based design optimization (RBDO) with the confidence level is proposed to offset the inaccurate estimation of the input model due to limited data by using an upper bound of confidence interval of the standard deviation. Using the upper bound of the confidence interval of the standard deviation, the confidence level of the input model can be assessed to obtain the confidence level of the output performance, i.e. a desired probability of failure, through the simulation-based design. For RBDO, the estimated input model with the associated confidence level is integrated with the most probable point (MPP)-based dimension reduction method (DRM), which improves accuracy over the first order reliability method (FORM). A mathematical example and a fatigue problem are used to illustrate how the input model with confidence level yields a reliable optimum design by comparing it with the input model obtained using the estimated parameters.

Confidence-Based Method for Reliability-Based Design Optimization

2014 ◽  
Author(s):  
Hyunkyoo Cho ◽  
K. K. Choi ◽  
David Lamb
Keyword(s):  
Design Optimization ◽  
Confidence Level ◽  
Probabilistic Model ◽  
Input Data ◽  
Mathematical Problem ◽  
Design Sensitivity ◽  
Conditional Probabilities ◽  
Probabilistic Constraint ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design

An accurate input probabilistic model is necessary to obtain a trustworthy result in the reliability analysis and the reliability-based design optimization (RBDO). However, the accurate input probabilistic model is not always available. Very often only insufficient input data are available in practical engineering problems. When only the limited input data are provided, uncertainty is induced in the input probabilistic model and this uncertainty propagates to the reliability output which is defined as the probability of failure. Then, the confidence level of the reliability output will decrease. To resolve this problem, the reliability output is considered to have a probability distribution in this paper. The probability of the reliability output is obtained as a combination of consecutive conditional probabilities of input distribution type and parameters using Bayesian approach. The conditional probabilities that are obtained under certain assumptions and Monte Carlo simulation (MCS) method is used to calculate the probability of the reliability output. Using the probability of the reliability output as constraint, a confidence-based RBDO (C-RBDO) problem is formulated. In the new probabilistic constraint of the C-RBDO formulation, two threshold values of the target reliability output and the target confidence level are used. For effective C-RBDO process, the design sensitivity of the new probabilistic constraint is derived. The C-RBDO is performed for a mathematical problem with different numbers of input data and the result shows that C-RBDO optimum designs incorporate appropriate conservativeness according to the given input data.

scholarly journals THE PHYSICAL SIGNIFICANCE OF CONFIDENCE INTERVALS

2001 ◽  
Vol 12 (08) ◽  
pp. 1155-1168 ◽  
Author(s):  
CARLO GIUNTI ◽  
MARCO LAVEDER
Keyword(s):  
Standard Deviation ◽  
Confidence Intervals ◽  
Confidence Level ◽  
Physical Significance ◽  
Expectation Value ◽  
Upper Limit ◽  
Null Result ◽  
Lower Detection ◽  
Definition Of ◽  
The Given

We define some appropriate statistical quantities that indicate the physical significance (reliability) of confidence intervals in the framework of both Frequentist and Bayesian statistical theories. We consider the expectation value of the upper limit in the absence of a signal (that we propose to call "exclusion potential", instead of "sensitivity" as done by Feldman and Cousins) and its standard deviation, we define the "Pull" of a null result, expressing the reliability of an experimental upper limit, and the "upper and lower detection functions", that give information on the possible outcome of an experiment if there is a signal. We also give a new appropriate definition of "sensitivity", that quantifies the capability of an experiment to reveal the signal that is searched for at the given confidence level.

Time-Dependent Concurrent Reliability-Based Design Optimization Integrating the Time-Variant B-Distance Index

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Zhonglai Wang ◽  
Zhihua Wang ◽  
Shui Yu ◽  
Xiaowen Cheng
Keyword(s):  
Experimental Data ◽  
Design Optimization ◽  
Confidence Level ◽  
Moment Method ◽  
Extreme Values ◽  
Time Dependent ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Speed Up ◽  
High Computational Efficiency

This paper presents a time-dependent concurrent reliability-based design optimization (TDC-RBDO) method integrating the time-variant B-distance index to improve the confidence level of design results with a small amount of experimental data. The time-variant B-distance index is first constructed using the extreme values of responses. The Hist Loop CDF (HLCDF) algorithm is then presented to calculate the time-variant B-distance index with high computational efficiency. The TDC-RBDO framework is provided by integrating the time-variant B-distance index and time-dependent reliability. The extreme value moment method (EVMM) is implemented to speed up the procedure of the TDC-RBDO. The case of a harmonic reducer is employed to elaborate on the proposed method.

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