Reliability sensitivity analysis method for time-dependent problem based on first-passage method

2018 ◽  
Vol 233 (4) ◽  
pp. 787-802
Author(s):  
Wenxuan Wang ◽  
Hangshan Gao ◽  
Changcong Zhou ◽  
Wanghua Xu
Keyword(s):  
Sensitivity Analysis ◽  
Time Dependent ◽  
Sensitivity Index ◽  
Analysis Method ◽  
First Passage ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Sensitivity Analysis Method ◽  
Global Reliability Sensitivity ◽  
The Impact

The sensitivity index plays a critical role in the design of product and is used to quantify the impact degree of the uncertainty of the input variable to the uncertainty of the interest output. This paper presents a new local reliability sensitivity method and a global reliability sensitivity analysis method of time-dependent reliability problems. Firstly, according to the Poisson's assumption-based first-passage method, the local reliability sensitivity index is directly obtained by calculating the partial derivative of the failure probability to the distribution parameters of input random variable. Then, the moment-independent global reliability sensitivity index of the time-dependent problems is derived based on the concept of moment-independent. Finally, the efficiency and accuracy of the proposed method are verified with the reference results of Monte Carlo simulation.

Extending the global reliability sensitivity analysis to the systems under double-stochastic uncertainty

2018 ◽  
Vol 22 (3) ◽  
pp. 626-640 ◽  
Author(s):  
Wenxuan Wang ◽  
Hangshan Gao ◽  
Changcong Zhou
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic System ◽  
Dynamic Strength ◽  
Point Estimation ◽  
Coupling Mechanism ◽  
Analysis Method ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Sensitivity Analysis Method ◽  
Global Reliability Sensitivity

Systems with random variables and random excitations exist widely in various engineering problems. Extending the traditional global reliability sensitivity to this double-stochastic system has important guiding significance for its design optimization. However, because there is a certain coupling between the randomness of variables and the randomness of excitation, this coupling mechanism is difficult to determine in practical projects. Therefore, it is difficult to extend the traditional reliability sensitivity analysis method to this double-stochastic system. In this research, it is assumed that there is no correlation between variables and excitations. Then, combining the first-passage method–based dynamic strength formula and the variance-based sensitivity analysis method, an approximate global reliability sensitivity analysis method for this double-stochastic system is proposed. In order to improve the computational efficiency, a nested loop method based on seven-point estimation is proposed for reliability sensitivity analysis. In order to verify the accuracy and efficiency of the proposed method, a Monte Carlo simulation is given as a reference. Three examples are studied and discussed to illustrate the practicality and feasibility of the proposed method.

A Fast Approximate Method for Reliability Sensitivity Based on Markov Chain Simulation

2007 ◽  
Vol 353-358 ◽  
pp. 1005-1008
Author(s):  
Xiu Kai Yuan ◽  
Zhen Zhou Lu
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Markov Chain ◽  
Weighted Regression ◽  
Analysis Method ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Failure Region ◽  
Sensitivity Analysis Method ◽  
Distribution Parameters

On the basis of Markov chain simulation, an efficient method is presented to analyze reliability sensitivity of structure. In the presented method, Markov chain is employed to draw the samples distributed in the failure region, and these samples are fitted in a form of hyperplane by the weighted regression. By use of the regressed hyperplane, it is convenient to complete the sensitivities of the failure probability with respect to the distribution parameters of basic random variables by the available method. The presented method is applied to some examples to validate its accuracy and efficiency. The obtained results show that the presented reliability sensitivity analysis method is far more efficient than Monte Carlo based method.

Sensitivity Analysis and Numerical Simulation of the Planar Mechanism Kinematic Accuracy

2012 ◽  
Vol 605-607 ◽  
pp. 1460-1464
Author(s):  
Rui Jun Zhang ◽  
Qing Xuan Jia ◽  
Liu Meng ◽  
Ji Wei Qiu
Keyword(s):  
Numerical Simulation ◽  
Sensitivity Analysis ◽  
Simulation Method ◽  
Analysis Method ◽  
Planar Mechanism ◽  
Kinematic Accuracy ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Sensitivity Analysis Method ◽  
Mechanism Kinematic

A kind of accuracy sensitivity analysis and numerical simulation method about the mechanism kinematic is proposed for the purpose of getting the reliability sensitivity of the planar mechanism. Combining with the reliability sensitivity analysis method and the computer simulation technology, this method is on the basis of the research of the kinematics equations and the mechanism kinematic accuracy model. Taking the kinematic accuracy sensitivity analysis of the planar four-bar mechanism as an example, the results verify the effectiveness and feasibility of the method.

Reliability Sensitivity Analysis for Differential Expansion of Steam Turbine by Saddlepoint Approximation

2012 ◽  
Vol 479-481 ◽  
pp. 1018-1022
Author(s):  
Le Xin Li ◽  
Chang Qing Su ◽  
Ya Juan Jin
Keyword(s):  
Sensitivity Analysis ◽  
Steam Turbine ◽  
Approximation Method ◽  
Saddlepoint Approximation ◽  
Analysis Method ◽  
Random Parameters ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Sensitivity Analysis Method ◽  
Differential Expansion

Based on Saddlepoint Approximation method and sensitivity analysis method, reliability sensitivity analysis for differential expansion of steam turbine with random parameters are studied. On the premise of the probability distribution of random parameters, using Saddlepoint Approximation method, probability density function of limit state function of differential expansion of steam turbine is obtained. The result of Saddlepoint Approximation method is very close to the one of Monte-Carlo, and the computing speed is fast. Then, the sensitivity analysis method and probability density function were employed to discuss the variation regularities of reliability sensitivity and the effect of design parameters on reliability of differential expansion of steam turbine is analyzed.

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