Performance of Subset Simulation Apllied to A Simple System Reliability Problem

2012 ◽  
Author(s):  
Jie Zhang ◽  
Hongwei Huang ◽  
Dongmei Zhang
Keyword(s):  
System Reliability ◽  
Simple System ◽  
Subset Simulation ◽  
Reliability Problem

Fatigue Reliability and Inspection of TLP Tendon System

1991 ◽  
Vol 28 (02) ◽  
pp. 99-110
Author(s):  
Maria Celia C. Ximenes
Keyword(s):  
Order Statistics ◽  
System Reliability ◽  
Fatigue Reliability ◽  
Tension Leg Platform ◽  
Reliability Problem ◽  
Failure Path ◽  
Solution Methods

The collapse of a tension leg platform (TLP) tendon system due to progressive fatigue of several joints is investigated. Two solution methods for the system reliability problem are proposed. The first method is based on the failure path approach and the second on the order statistics approach. The effects of redundancy and uncertainties involved in the problem are analyzed. Inspection of selected joints is also included in order to evaluate its impact on system reliability.

A Novel Time-Variant Reliability Analysis Method Based on Failure Processes Decomposition for Dynamic Uncertain Structures

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Shui Yu ◽  
Zhonglai Wang
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Dynamic Properties ◽  
Limit State ◽  
Quadratic Function ◽  
State Function ◽  
Analysis Method ◽  
Engineering Structures ◽  
Reliability Problem ◽  
Time Invariant

Abstract Due to the uncertainties and the dynamic parameters from design, manufacturing, and working conditions, many engineering structures usually show uncertain and dynamic properties. This paper proposes a novel time-variant reliability analysis method using failure processes decomposition to transform the time-variant reliability problems to the time-invariant problems for dynamic structures under uncertainties. The transformation is achieved via a two-stage failure processes decomposition. First, the limit state function with high dimensional input variables and high order temporal parameters is transformed to a quadratic function of time based on the optimized time point in the first-stage failure processes decomposition. Second, based on the characteristics of the quadratic function and reliability criterion, the time-variant reliability problem is then transformed to a time-invariant system reliability problem in the second-stage failure processes decomposition. Then, the kernel density estimation (KDE) method is finally employed for the system reliability evaluation. Several examples are used to verify the effectiveness of the proposed method to demonstrate its efficiency and accuracy.

A Time-Variant Reliability Analysis Method Based on Stochastic Process Discretization

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
C. Jiang ◽  
X. P. Huang ◽  
X. Han ◽  
D. Q. Zhang
Keyword(s):  
Stochastic Process ◽  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Complex Structure ◽  
Random Variable ◽  
State Function ◽  
Analysis Method ◽  
Reliability Problem ◽  
Reliability Method

Time-variant reliability problems caused by deterioration in material properties, dynamic load uncertainty, and other causes are widespread among practical engineering applications. This study proposes a novel time-variant reliability analysis method based on stochastic process discretization (TRPD), which provides an effective analytical tool for assessing design reliability over the whole lifecycle of a complex structure. Using time discretization, a stochastic process can be converted into random variables, thereby transforming a time-variant reliability problem into a conventional time-invariant system reliability problem. By linearizing the limit-state function with the first-order reliability method (FORM) and furthermore, introducing a new random variable, the converted system reliability problem can be efficiently solved. The TRPD avoids the calculation of outcrossing rates, which simplifies the process of solving time-variant reliability problems and produces high computational efficiency. Finally, three numerical examples are used to verify the effectiveness of this approach.

The negative log-gamma prior distribution for Bayesian assessment of system reliability

2017 ◽  
Vol 232 (3) ◽  
pp. 308-319 ◽  
Author(s):  
Roger Zoh ◽  
Alyson Wilson ◽  
Scott Vander Wiel ◽  
Earl Lawrence
Keyword(s):  
Data Collection ◽  
Gamma Distribution ◽  
System Reliability ◽  
Prior Distribution ◽  
Scale Parameter ◽  
Time Varying ◽  
Prior Distributions ◽  
Reliability Problem ◽  
Series Systems

This paper presents the negative log-gamma distribution as a prior distribution useful for Bayesian assessment of system reliability. When the scale parameter is held fixed, the negative log-gamma distribution is closed under products, making it convenient for specifying priors for series systems. In particular, for series systems, negative log-gamma component priors can be specified to give an exact desired system prior and vice versa. We consider pass/fail data at the system and component levels for both static and time-varying data collection schemes and propose two new prior distributions for analyzing time-varying reliability. Finally, we consider an application of the negative log-gamma to a missile reliability problem and illustrate diagnostics useful for developing the priors.

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