System-reliability confidence-intervals for complex-systems with estimated component-reliability

1997 ◽  
Vol 46 (4) ◽  
pp. 487-493 ◽  
Author(s):  
D.W. Coit
Keyword(s):  
Complex Systems ◽  
Confidence Intervals ◽  
System Reliability ◽  
Component Reliability

A Partial Safety Factor Method for System Reliability Prediction With Outsourced Components

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhengwei Hu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Joint Probability ◽  
System Level ◽  
Safety Factors ◽  
Component Reliability ◽  
Reliability Method ◽  
Component Supplier ◽  
Partial Safety Factors ◽  
Joint Probability Density ◽  
Probability Density Function Pdf

System reliability is usually predicted with the assumption that all component states are independent. This assumption may not accurate for systems with outsourced components since their states are strongly dependent and component details may be unknown. The purpose of this study is to develop an accurate system reliability method that can produce complete joint probability density function (PDF) of all the component states, thereby leading to accurate system reliability predictions. The proposed method works for systems whose failures are caused by excessive loading. In addition to the component reliability, system designers also ask for partial safety factors for shared loadings from component suppliers. The information is then sufficient for building a system-level joint PDF. Algorithms are designed for a component supplier to generate partial safety factors. The method enables accurate system reliability predictions without requiring proprietary information from component suppliers.

Reliability Model for Complex Mechanical Component Design

2013 ◽  
Vol 365-366 ◽  
pp. 28-31
Author(s):  
Li Yang Xie ◽  
Wen Xue Qian ◽  
Ning Xiang Wu
Keyword(s):  
System Reliability ◽  
Failure Modes ◽  
Statistical Dependence ◽  
Series System ◽  
Reliability Model ◽  
Component Reliability ◽  
Damage Site ◽  
Mechanical Component ◽  
Component Design ◽  
Element Failure

Taking into account the uncertainty in material property and component quality, a complex mechanical component such as a gear should be treated as a series system instead of a component when evaluating its reliability, since there exist many sites of equal likelihood to fail. Besides, conventional system reliability model is not applicable to such a system because of the statistical dependence among the failures of the every element (damage site). The present paper presents a model to estimate complex mechanical component reliability by incorporating order statistic of element strength into load-strength interference analysis, which can deal with multiple failure mechanisms, reflect statistical dependence among element failure events and that among different failure modes.

A Novel Fault Tree Analysis Theory

2013 ◽  
Vol 779-780 ◽  
pp. 1711-1714
Author(s):  
Yuan Liang Huang ◽  
Jia Qi Zhong
Keyword(s):  
Complex Systems ◽  
System Reliability ◽  
Logic Gate ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Analysis Theory ◽  
Software And Hardware ◽  
Grey Number

A novel fault tree analysis theory is introduced for the ambiguity in complex systems. In the theory, the frequency grey number, which can express the events subjective ambiguity and objective ambiguity, is introduced to express the degree and probability that the components go wrong, dynamic envelope is applied to score the relation among components, and a new logic gate, Grey-gate, is advanced for expressing the effect of system reliability when the components go wrong. Finally, the theory is applied to analyze the fault effect of the system with software and hardware.

scholarly journals Is this scaling nonlinear?

2016 ◽  
Vol 3 (7) ◽  
pp. 150649 ◽  
Author(s):  
J. C. Leitão ◽  
J. M. Miotto ◽  
M. Gerlach ◽  
E. G. Altmann
Keyword(s):  
Complex Systems ◽  
Confidence Intervals ◽  
Probabilistic Framework ◽  
Heavy Tailed ◽  
The City

One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g. patents) scale nonlinearly with the population x of the cities in which they appear, i.e. y ∼ x β , β ≠1. More recently, the generality of this finding has been questioned in studies that used new databases and different definitions of city boundaries. In this paper, we investigate the existence of nonlinear scaling, using a probabilistic framework in which fluctuations are accounted for explicitly. In particular, we show that this allows not only to (i) estimate β and confidence intervals, but also to (ii) quantify the evidence in favour of β ≠1 and (iii) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare five different models to 15 different datasets and we find that the answers to points (i)–(iii) crucially depend on the fluctuations contained in the data, on how they are modelled, and on the fact that the city sizes are heavy-tailed distributed.

scholarly journals A New System Reliability Optimization Model Based on Swapping Existing Components

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yuxiong Li ◽  
Xianzhen Huang ◽  
Xinong En ◽  
Pengfei Ding
Keyword(s):  
Optimization Model ◽  
System Reliability ◽  
Original System ◽  
Reliability Optimization ◽  
Component Reliability ◽  
Bee Colony ◽  
Efficient Calculation ◽  
System Reliability Optimization ◽  
New System ◽  
The Ideal

Complex systems contain a large number of components, and in some cases, failure of one or more of these components can cause the entire system to fail. Replacing failed components with other functioning components properly in the original system can be an attractive way for improving system reliability. This paper proposes a new system reliability optimization model to achieve optimal component reliability and the ideal component-swapping strategy under a certain set of constraints. Furthermore, the survival signature is introduced to more efficient calculation of system reliability under various component-swapping cases, and an artificial bee colony (ABC) algorithm with local search method for component swapping is applied to solve the optimization problem. Finally, numerical examples are presented to illustrate the optimization process.

Sign in / Sign up

Export Citation Format

Share Document