Analysis of Reliability and Reliability Sensitivity for Machine Components by Mean-value First Order Saddlepoint Approximation

2009 ◽  
Vol 45 (12) ◽  
pp. 102 ◽  
Author(s):  
Yajuan JIN
Keyword(s):  
Saddlepoint Approximation ◽  
Mean Value ◽  
First Order ◽  
Reliability Sensitivity

Unified uncertainty analysis by the mean value first order saddlepoint approximation

2012 ◽  
Vol 46 (6) ◽  
pp. 803-812 ◽  
Author(s):  
Ning-Cong Xiao ◽  
Hong-Zhong Huang ◽  
Zhonglai Wang ◽  
Yu Liu ◽  
Xiao-Ling Zhang
Keyword(s):  
Uncertainty Analysis ◽  
Saddlepoint Approximation ◽  
Mean Value ◽  
First Order ◽  
The Mean

A Mean Value Reliability Method for Bimodal Distributions

2017 ◽  
Author(s):  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Probability Density ◽  
Random Variables ◽  
Saddlepoint Approximation ◽  
Random Variable ◽  
Mean Value ◽  
Traffic Load ◽  
First Order ◽  
Reliability Method ◽  
Basic Random Variable ◽  
Two Peaks

In traditional reliability problems, the distribution of a basic random variable is usually unimodal; in other words, the probability density of the basic random variable has only one peak. In real applications, some basic random variables may follow bimodal distributions with two peaks in their probability density. For example, the random load of a bridge may have two peaks, with a distribution consisting of a weighted sum of two normal distributions, suggested by traffic load data. When binomial variables are involved, traditional reliability methods, such as the First Order Second Moment (FOSM) method and the First Order Reliability Method (FORM), will not be accurate. This study investigates the accuracy of using the saddlepoint approximation for bimodal variables and then employs a mean value reliability method to accurately predict the reliability. A limit-state function is at first approximated with the first order Taylor expansion so that it becomes a linear combination of the basic random variables, some of which are bimodally distributed. The saddlepoint approximation is then applied to estimate the reliability. Examples show that the new method is more accurate than FOSM and FORM.

Reliability sensitivity based on first-order reliability method

2011 ◽  
Vol 225 (9) ◽  
pp. 2189-2197 ◽  
Author(s):  
Z Yanfang ◽  
Z Yanlin ◽  
Z Yimin
Keyword(s):  
Sensitivity Analysis ◽  
Limit State ◽  
Approximate Solutions ◽  
Accurate Method ◽  
Mean Value ◽  
Numerical Results ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Sensitivity ◽  
Reliability Method

A new efficient and accurate method for reliability sensitivity analysis of mechanical components is proposed based on the first-order reliability method (FORM). This method provides very close approximate solutions for strongly non-linear limit state functions with independent normal random variables. But most probable point should be searched at first. Three methods are presented and investigated for reliability-based sensitivity analysis. They are, respectively, based on three different reliability analysis methods which are mean-value first-order reliability method, Monte Carlo method, and FORM. Numerical results of the three methods are calculated out by three examples. The accuracy and efficiency of the new method are demonstrated by the comparison of the numerical results.

Some Important Issues on First-Order Reliability Analysis With Nonprobabilistic Convex Models

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
C. Jiang ◽  
G. Y. Lu ◽  
X. Han ◽  
R. G. Bi
Keyword(s):  
Reliability Analysis ◽  
Reliability Index ◽  
Probability Model ◽  
Mean Value ◽  
Convex Model ◽  
First Order ◽  
Reliability Method ◽  
Complex Engineering ◽  
Mean Value Method ◽  
Form Type

Compared with the probability model, the convex model approach only requires the bound information on the uncertainty, and can make it possible to conduct the reliability analysis for many complex engineering problems with limited samples. Presently, by introducing the well-established techniques in probability-based reliability analysis, some methods have been successfully developed for convex model reliability. This paper aims to reveal some different phenomena and furthermore some severe paradoxes when extending the widely used first-order reliability method (FORM) into the convex model problems, and whereby provide some useful suggestions and guidelines for convex-model-based reliability analysis. Two FORM-type approximations, namely, the mean-value method and the design-point method, are formulated to efficiently compute the nonprobabilistic reliability index. A comparison is then conducted between these two methods, and some important phenomena different from the traditional FORMs are summarized. The nonprobabilistic reliability index is also extended to treat the system reliability, and some unexpected paradoxes are found through two numerical examples.

scholarly journals TOWARDS DIFFERENTIAL CALCULUS IN STRATIFIED GROUPS

2013 ◽  
Vol 95 (1) ◽  
pp. 76-128 ◽  
Author(s):  
VALENTINO MAGNANI
Keyword(s):  
Implicit Function Theorem ◽  
Group Structure ◽  
Mean Value ◽  
Implicit Function ◽  
Inverse Mapping ◽  
Natural Group ◽  
Stratified Groups ◽  
First Order ◽  
Inverse Mapping Theorem ◽  
Rank Theorem

AbstractWe study graded group-valued continuously differentiable mappings defined on stratified groups, where differentiability is understood with respect to the group structure. We characterize these mappings by a system of nonlinear first-order PDEs, establishing a quantitative estimate for their difference quotient. This provides us with a mean value estimate that allows us to prove both the inverse mapping theorem and the implicit function theorem. The latter theorem also relies on the fact that the differential admits a proper factorization of the domain into a suitable inner semidirect product. When this splitting property of the differential holds in the target group, then the inverse mapping theorem leads us to the rank theorem. Both implicit function theorem and rank theorem naturally introduce the classes of image sets and level sets. For commutative groups, these two classes of sets coincide and correspond to the usual submanifolds. In noncommutative groups, we have two distinct classes of intrinsic submanifolds. They constitute the so-called intrinsic graphs, that are defined with respect to the algebraic splitting and everywhere possess a unique metric tangent cone equipped with a natural group structure.

Second-Order Reliability Method With First-Order Efficiency

2010 ◽  
Author(s):  
Xiaoping Du ◽  
Junfu Zhang
Keyword(s):  
Limit State ◽  
Saddlepoint Approximation ◽  
Quadratic Function ◽  
Second Order ◽  
State Function ◽  
Limit State Function ◽  
First Order ◽  
Reliability Method ◽  
Order Of Magnitude ◽  
Univariate Function

The widely used First Order Reliability Method (FORM) is efficient, but may not be accurate for nonlinear limit-state functions. The Second Order Reliability Method (SORM) is more accurate but less efficient. To maintain both high accuracy and efficiency, we propose a new second order reliability analysis method with first order efficiency. The method first performs the FORM and identifies the Most Probable Point (MPP). Then the associated limit-state function is decomposed into additive univariate functions at the MPP. Each univariate function is further approximated as a quadratic function, which is created with the gradient information at the MPP and one more point near the MPP. The cumulant generating function of the approximated limit-state function is then available so that saddlepoint approximation can be easily applied for computing the probability of failure. The accuracy of the new method is comparable to that of the SORM, and its efficiency is in the same order of magnitude as the FORM.

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