Reliability sensitivity analysis of coherent systems based on survival signature

The reliability sensitivity can be used to rank distribution parameters of system components concerning their impacts on the system’s reliability. Such information is essential to purposes such as component prioritization, reliability improvement, and risk reduction of a system. In this article, we present an efficient method for reliability sensitivity analysis of coherent systems using survival signature. The survival signature is applied to calculate the reliability of coherent systems. The reliability importance of components is derived analytically to evaluate the relative importance of the component with respect to the overall reliability of the system. The closed-form formula for the reliability sensitivity of the system with respect to component’s distribution parameters is derived from the derivative of lifetime distribution of a component to further investigate the impacts of the distribution parameters on the system’s reliability. The effectiveness and feasibility of the proposed approaches are demonstrated with two numerical examples.

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Reliability Sensitivity Analysis Method for Mechanical Components

Mathematical Problems in Engineering ◽

10.1155/2021/7867003 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yan-Fang Zhang ◽

Yan-Lin Zhang

Keyword(s):

Sensitivity Analysis ◽

Failure Probability ◽

Reduction Method ◽

Dimension Reduction Method ◽

Edgeworth Series ◽

Reliability Sensitivity ◽

Reliability Sensitivity Analysis ◽

Distribution Parameters ◽

Mechanical Components ◽

Univariate Dimension Reduction Method

Based on the univariate dimension-reduction method (UDRM), Edgeworth series, and sensitivity analysis, a new method for reliability sensitivity analysis of mechanical components is proposed. The univariate dimension-reduction method is applied to calculate the response origin moments and their sensitivity with respect to distribution parameters (e.g., mean and standard deviation) of fundamental input random variables. Edgeworth series is used to estimate failure probability of mechanical components by using first few response central moments. The analytic formula of reliability sensitivity can be derived by calculating partial derivative of the failure probability P f with respect to distribution parameters of basic random variables. The nonnormal random parameters need not to be transformed into equivalent normal ones. Three numerical examples are employed to illustrate the accuracy and efficiency of the proposed method by comparing the failure probability and reliability sensitivity results obtained by the proposed method with those obtained by Monte Carlo simulation (MCS).

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

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.353-358.1005 ◽

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.

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Parametric Reliability Sensitivity Analysis Using Failure Probability Ratio Function

International Journal of Computational Methods ◽

10.1142/s021987621641005x ◽

2016 ◽

Vol 13 (04) ◽

pp. 1641005 ◽

Cited By ~ 1

Author(s):

Pengfei Wei ◽

Zhenzhoug Lu

Keyword(s):

Sensitivity Analysis ◽

Importance Sampling ◽

Failure Probability ◽

Probability Ratio ◽

Sensitivity Indices ◽

Analysis Technique ◽

Parametric Reliability ◽

Reliability Sensitivity ◽

Reliability Sensitivity Analysis ◽

Distribution Parameters

Reducing the failure probability is an important task in the design of engineering structures. In this paper, a reliability sensitivity analysis technique, called failure probability ratio function, is firstly developed for providing the analysts quantitative information on failure probability reduction while one or a set of distribution parameters of model inputs are changed. Then, based on the failure probability ratio function, a global sensitivity analysis technique, called R-index, is proposed for measuring the average contribution of the distribution parameters to the failure probability while they vary in intervals. The proposed failure probability ratio function and R-index can be especially useful for failure probability reduction, reliability-based optimization and reduction of the epistemic uncertainty of parameters. The Monte Carlo simulation (MCS), Importance Sampling (IS) and Truncated Importance Sampling (TIS) procedures, which need only a set of samples for implementing them, are introduced for efficiently computing the proposed sensitivity indices. A numerical example is introduced for illustrating the engineering significance of the proposed sensitivity indices and verifying the efficiency and accuracy of the MCS, IS and TIS procedures. At last, the proposed sensitivity techniques are applied to a planar 10-bar structure for achieving a targeted 80% reduction of the failure probability.

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