scholarly journals Causal Algebras on Chain Event Graphs with Informed Missingness for System Failure

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1308
Author(s):  
Xuewen Yu ◽  
Jim Q. Smith
Keyword(s):  
Energy Distribution ◽  
System Reliability ◽  
Causal Reasoning ◽  
Graphical Model ◽  
Causal Analysis ◽  
System Failure ◽  
Event Tree ◽  
Causal Modelling ◽  
Remedial Intervention ◽  
A Chain

Graph-based causal inference has recently been successfully applied to explore system reliability and to predict failures in order to improve systems. One popular causal analysis following Pearl and Spirtes et al. to study causal relationships embedded in a system is to use a Bayesian network (BN). However, certain causal constructions that are particularly pertinent to the study of reliability are difficult to express fully through a BN. Our recent work demonstrated the flexibility of using a Chain Event Graph (CEG) instead to capture causal reasoning embedded within engineers’ reports. We demonstrated that an event tree rather than a BN could provide an alternative framework that could capture most of the causal concepts needed within this domain. In particular, a causal calculus for a specific type of intervention, called a remedial intervention, was devised on this tree-like graph. In this paper, we extend the use of this framework to show that not only remedial maintenance interventions but also interventions associated with routine maintenance can be well-defined using this alternative class of graphical model. We also show that the complexity in making inference about the potential relationships between causes and failures in a missing data situation in the domain of system reliability can be elegantly addressed using this new methodology. Causal modelling using a CEG is illustrated through examples drawn from the study of reliability of an energy distribution network.

scholarly journals Reliability Assessment for a Safety-Related Digital Reactor Protection System Using Event-Tree/Fault-Tree (ET/FT) Method

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qingzhu Liang ◽  
Mingxing Liu ◽  
Peng Xiao ◽  
Yun Guo ◽  
Jun Xiao ◽  
...  
Keyword(s):  
System Design ◽  
Upper Bound ◽  
System Reliability ◽  
Nuclear Power ◽  
Power Plants ◽  
Fault Tree ◽  
Sensitivity Analyses ◽  
System Failure ◽  
Event Tree ◽  
Reliability Modeling

The aim of this study is to verify if the reliability of a digital four-channel RPS under the design phase satisfies the specified target and to identify the weakness of system design and potential solutions for system reliability improvement. The event-tree/fault-tree (ET/FT), which is the method used in the current probabilistic safety assessment (PSA) framework of nuclear power plants (NPPs), was adopted to developed reliability modeling for the RPS with the Top Events defined as the system failure to generate reactor trip signal and the system generating spurious trip signal. The evaluation results indicate that the probability of the system failure on demand and the frequency of spurious trip signal generation are 1.47 × 10−6 with a 95% upper bound of 4.63 × 10−6 and 7.94 × 10−4/year with a 95% upper bound of 2.50 × 10−3/year, respectively. The importance and sensitivity analyses were conducted and it was found that undetected unsafe common cause failures (CCFs) of signal conditioning modules (SCMs) dominate the system reliability. Two preliminary optimization schemes relative to reducing periodic test interval and adapting two kinds of diverse SCMs were proposed. Results of the quantitive evaluation of the schemes show that neither of them could determinedly improve the system reliability to the target level. In the future, more detailed optimization analysis shall be required to determine a feasible system design optimization scheme.

scholarly journals An Overview of Various Importance Measures of Reliability System

2017 ◽  
Vol 2 (3) ◽  
pp. 150-171 ◽  
Author(s):  
Kalpesh P. Amrutkar ◽  
Kirtee K. Kamalja
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
System Failure ◽  
Numerical Rank ◽  
Reliability Improvement ◽  
System Reliability Analysis ◽  
Component Importance ◽  
Critical Components ◽  
The Impact ◽  
Reliability Systems

One of the purposes of system reliability analysis is to identify the weaknesses or the critical components in a system and to quantify the impact of component’s failures. Various importance measures are being introduced by many researchers since 1969. These component importance measures provide a numerical rank to determine which components are more important to system reliability improvement or more critical to system failure. In this paper, we overview various components importance measures and briefly discuss them with examples. We also discuss some other extended importance measures and review the developments in study of various importance measures with respect to some of the popular reliability systems.

Reliability of PCCS in AP1000 Based on Accident Development

2016 ◽  
Author(s):  
Yu Yu ◽  
Shengfei Wang ◽  
Fenglei Niu
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Physical Process ◽  
System Reliability ◽  
Nuclear Power ◽  
Cooling System ◽  
Natural Circulation ◽  
System Structure ◽  
System Failure ◽  
Process Failure

Passive containment cooling system (PCCS) is an important safety-related system in AP1000 nuclear power plant, by which heat produced in reactor is transferred to the heat sink – atmosphere – based on natural circulation, independent of human response or the operation of outside equipments, so the reactor capacity of resisting external hazards (earthquake, flood, etc.) is improved. However since the system operation based on natural circulation, many uncertainty factors such as temperatures of cold and heat sources will affect the system reliability, and physical process failure becomes one of the important contributors to system failure, which is not considered in the active system reliability analysis. That is, the system will lose its function since the natural circulation cannot be established or kept even when the equipments in the system can work well. The function of PCCS in AP1000 is to transfer the heat produced in the containment to the environment and to keep the pressure in the containment below its threshold. After accidents the steam is injected to the containment and can be cooled and condensed when it arrives at the containment wall, then the heat is transferred to the atmosphere through the steel vessel. So the peak value of the pressure is influenced by the steam situation which is injected into the containment and the heat transfer and condensate processes under the accidents. In this paper the dynamic thermal-hydraulic (T-H) model simulating the fluid performance in the containment is established, based on which the system reliability model is built. Here the total pressure in the containment is used as the success criteria. Apparently the system physical process failure may be related to the system working state, the outside conditions, the system structure parameters and so on, and it’s a heavy work to analyze the influences of all the factors, so only the effects of important ones are included in the model. Monte Carlo (MC) simulation is used to evaluate the system reliability, in which the input parameters such as air temperature are sampled based on their probabilistic density distributions. The pressure curves along with the accident development are gained and the system reliabilities under different accidents are gotten as well as the main contributors. The results illustrate that the system physical process failure probabilities are varied under different climate conditions, which result in the system reliability and the main contributors to system failure changing, so the different methods can be taken to improve the system reliability according to the local condition of the nuclear power plant.

Extreme Value Metamodeling for System Reliability With Time-Dependent Functions

2015 ◽  
Author(s):  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
Computational Efficiency ◽  
System Reliability ◽  
Extreme Values ◽  
Random Variables ◽  
Probability Of Failure ◽  
High Accuracy ◽  
Extreme Value ◽  
Time Dependent ◽  
System Failure ◽  
Kriging Method

The reliability of a system is usually measured by the probability that the system performs its intended function in a given period of time. Estimating such reliability is a challenging task when the probability of failure is rare and the responses are nonlinear and time variant. The evaluation of the system reliability defined in a period of time requires the extreme values of the responses in the predefined period of time during which the system is supposed to function. This work builds surrogate models for the extreme values of responses with the Kriging method. For the sake of computational efficiency, the method creates Kriging models with high accuracy only in the region that has high contributions to the system failure; training points of random variables and time are sampled simultaneously so that their interactions could be considered automatically. The example of a mechanism system shows the effectiveness of the proposed method.

SYSTEM RELIABILITY GROWTH ANALYSIS USING COMPONENT FAILURE DATA

1994 ◽  
Vol 01 (01) ◽  
pp. 71-83 ◽  
Author(s):  
M. XIE ◽  
T.N. GOH
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Simple Approach ◽  
System Level ◽  
Reliability Growth ◽  
System Failure ◽  
Component Level ◽  
Component Failure ◽  
Failure Data ◽  
Growth Estimation

In this paper the problem of system-level reliability growth estimation using component-level failure data is studied. It is suggested that system failure data should be broken down into component, or subsystem, failure data when the above problems have occurred during the system testing phase. The proposed approach is especially useful when the system is not unchanged over the time, when some subsystems are improved more than others, or when the testing has been concentrated on different components at different time. These situations usually happen in practice and it may also be the case even if the system failure data is provided. Two sets of data are used to illustrate the simple approach; one is a set of component failure data for which all subsystems are available for testing at the same time and for the other set of data, the starting times are different for different subsystems.

Test and Diagnosis of Automotive Embedded Processors via High-Speed Standard Interfaces

2017 ◽  
Vol 26 (08) ◽  
pp. 1740006
Author(s):  
Christian Gleichner ◽  
Heinrich T. Vierhaus
Keyword(s):  
Integrated Circuits ◽  
Embedded System ◽  
System Reliability ◽  
High Speed ◽  
State Of The Art ◽  
Fault Analysis ◽  
Production Test ◽  
System Failure ◽  
Functional Tests ◽  
The Embedded System

In state-of-the-art automotive controllers, functional tests are used to check their integrity in the field. Features dedicated to production test of integrated circuits such as scan chains are not applied in the embedded system. However, such test structures enable a more effective and diagnostic test, which improves the fault analysis in case of a system failure and even increases system reliability. To achieve this, an access to the integrated test logic is required. This paper describes a concept of a test access to embedded systems via high-speed standard interfaces. The extended test logic as well as an appropriate test routine is presented.

Reliability Interval Estimation for a System Model with Element Duplication in Different Subsystems

2020 ◽  
pp. 4-13
Author(s):  
I.V. Pavlov ◽  
L.K. Gordeev
Keyword(s):  
System Reliability ◽  
Sliding Mode ◽  
Interval Estimation ◽  
Computational Procedure ◽  
Reliability Function ◽  
System Model ◽  
System Failure ◽  
Reliability Parameters ◽  
Lower Confidence ◽  
Analytical Expressions

The problem was considered of estimating reliability for a complex system model with element duplication of various subsystems and ensuring possibility of additional redundancy in a more flexible dynamic (or 'sliding') mode in each of the subsystems, which significantly increases reliability of the system in general. For the system considered, general model and analytical expressions were obtained in regard to the main reliability indicators, i.e., probability of the system failure-free operation (reliability function) for a given time and mean time of the system failure-free operation. On the basis of these analytical expressions, the lower confidence limit for the system reliability function was found in a situation, where the element reliability parameters were unknown, and only results of testing the system elements for reliability were provided. It was shown that the system resource function was convex in the reliability parameters vector of the system separate elements various types. Based on this, the lower confidence boundary construction for the system reliability function was reduced to the problem of finding the convex function extremum on a confidence set in the system element parameter space. In this case, labor consumption of the corresponding computational procedure increases linearly with an increase in the problem dimension. Numerical examples of calculating the lower confidence boundary for the system reliability function were provided

A New Concept for Assessing System Reliability Based on Probability Decomposition Method

2007 ◽  
Vol 353-358 ◽  
pp. 2525-2528
Author(s):  
Yang Pei ◽  
Bi Feng Song ◽  
Qing Han
Keyword(s):  
Failure Probability ◽  
System Reliability ◽  
Decomposition Method ◽  
Fault Tree Analysis ◽  
System Failure ◽  
Reliability Design ◽  
Critical States ◽  
Component Importance ◽  
Probability Concept ◽  
Probability Decomposition

In fault tree analysis, the system failure probability and the component importance measures cannot totally include the contribution of all the component existing states to system reliability. It is for this reason that an ‘equivalent’ failure probability concept is proposed. First, the system existing states are analyzed by probability decomposition method. Then Markov chain method and the expectation theory are used to calculate the expected working number resulting in system failure. And the system equivalent failure probability is finally attained. Analysis shows that: (1) equivalent failure probability not only includes the contribution of critical states of component to system reliability, but also the non-critical states of component are considered; and (2) it may provide a thorough assessment of system reliability and is useful for reliability design.

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