Uncertainty analysis of common cause failure in safety instrumented systems

2011 ◽  
Vol 225 (4) ◽  
pp. 450-460 ◽  
Author(s):  
W Mechri ◽  
C Simon ◽  
K Ben Othman
Keyword(s):  
Fuzzy Numbers ◽  
Epistemic Uncertainty ◽  
Monte Carlo Sampling ◽  
Fault Trees ◽  
Common Cause ◽  
Common Cause Failure ◽  
On Demand ◽  
Safety Integrity Level ◽  
Imperfect Knowledge ◽  
The Common

This paper analyses the problem of epistemic uncertainty in assessing the performance of safety instrumented systems (SIS) using fault trees. The imperfect knowledge concerns the common cause failure (CCF) involved in the SIS in low demand mode. The point-valued CCF factors are replaced by fuzzy numbers, allowing experts to express their uncertainty about the CCF values. This paper shows how these uncertainties propagate through the fault tree and how this induces an uncertainty to the values of the SIS failure probability on demand and to the safety integrity level of the SIS. For the sake of verification and comparison, and to show the exactness of the approach, a Monte Carlo sampling approach is proposed, where by a uniform or triangular second-order probability distribution of CCF factors is considered.

scholarly journals Effective sensor placement based on a VIKOR method considering common cause failure in the presence of epistemic uncertainty

2021 ◽  
Vol 23 (2) ◽  
pp. 253-262
Author(s):  
Rong-Xing Duan ◽  
Jie-Jun He ◽  
Tao Feng ◽  
Shu-Juan Huang ◽  
Li Chen
Keyword(s):  
System Reliability ◽  
Epistemic Uncertainty ◽  
Sensor Placement ◽  
Vikor Method ◽  
Common Cause ◽  
Reliability Indices ◽  
Common Cause Failure ◽  
Dynamic Fault Tree ◽  
Sensor Model

Owing to expensive cost and restricted structure, limited sensors are allowed to install in modern systems to monitor the working state, which can improve their availability. Therefore, an effective sensor placement method is presented based on a VIKOR algorithm considering common cause failure (CCF) under epistemic uncertainty in this paper. Specifically, a dynamic fault tree (DFT) is developed to build a fault model to simulate dynamic fault behaviors and some reliability indices are calculated using a dynamic evidence network (DEN). Furthermore, a VIKOR method is proposed to choose the possible sensor locations based on these indices. Besides, a sensor model is introduced by using a priority AND gate (PAND) to describe the failure sequence between a sensor and a component. All placement schemes can be enumerated when the number of sensors is given, and the largest system reliability is the best alternative among the placement schemes. Finally, a case study shows that CCF has some influence on sensor placement and cannot be neglected in the reliabilitybased sensor placement.

Optimal sensor placement based on multiattribute decision-making considering the common cause failure

2019 ◽  
Vol 233 (19-20) ◽  
pp. 7170-7182 ◽  
Author(s):  
Tao Feng ◽  
Rongxing Duan ◽  
Yanni Lin ◽  
Yining Zeng
Keyword(s):  
Decision Making ◽  
Sensor Placement ◽  
Fault Tree ◽  
Optimal Placement ◽  
Decision Matrix ◽  
Optimal Sensor Placement ◽  
Common Cause ◽  
Common Cause Failure ◽  
Dynamic Fault Tree ◽  
The Common

A new optimal sensor placement is developed to improve the efficiency of fault diagnosis based on multiattribute decision-making considering the common cause failure. The optimal placement scheme is selected based on the reliability of the top event on condition that the number of sensors is preset. Specifically, a β-factor model is introduced to deal with the common cause failure, and dynamic fault tree is used to describe the dynamic failure behaviors. Besides, a dynamic fault tree is converted into a dynamic Bayesian network to calculate the reliability parameters, which construct the decision matrix. Furthermore, an efficient TOPSIS algorithm is adopted to determine the potential locations of sensors. In addition, a diagnostic sensor model is developed to take into account the failure sequence between a sensor and a component using a priority AND gate, and the failure probability of the top event for all sensor placement scenarios is calculated to determine the optimal sensor placement. Finally, a case is provided to prove that the common cause failure has made a considerable impact on the sensor placement.

Common cause failure model updating for risk monitoring in nuclear power plants based on alpha factor model

2017 ◽  
Vol 231 (3) ◽  
pp. 209-220 ◽  
Author(s):  
Min Zhang ◽  
Zhijian Zhang ◽  
Ali Mosleh ◽  
Sijuan Chen
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Model Updating ◽  
Factor Model ◽  
Failure Model ◽  
Common Cause ◽  
Common Cause Failure ◽  
Risk Monitoring ◽  
Common Cause Failures ◽  
The Common

Common cause failure model updating (both qualitatively and quantitatively) is a key factor in risk monitoring for nuclear power plants when configuration changes (e.g. components become unavailable) occur among a redundant configuration. This research focuses on the common cause failure updating based on the alpha factor model method, which is commonly used in the living probabilistic safety assessment models for nuclear power plant risk monitoring. This article first discusses the common cause failure model updating in an ideal condition, which evaluates the common cause failure model parameters for the configurationally changed system in different ways, based on the causes of the detected failures. Then, two alternative updating processes are proposed considering the difficulty to identify failure causes immediately during plant operation: one is to update the common cause failure models with the assumption that the failures detected are independent failures and the other is to update the common cause failure models with the parameters as expectations of the values for all possible failure causes. Finally, a case study is given to illustrate the common cause failure updating process and to compare these two alternative processes. The results show that (1) common cause failures can be reevaluated automatically by the methods proposed in this article and (2) the second process is more conservative and reasonable but with more data requirements compared with the first approach. Considering limitations in accessibility of the data, the first strategy is suggested currently. More future work on data acquisition is demanded for better assessment of common cause failures during nuclear power plant risk monitoring.

Evidential network-based failure analysis for systems suffering common cause failure and model parameter uncertainty

2018 ◽  
Vol 233 (6) ◽  
pp. 2225-2235 ◽  
Author(s):  
Lin Zuo ◽  
Tangfan Xiahou ◽  
Yu Liu
Keyword(s):  
Failure Analysis ◽  
Network Model ◽  
Parameter Uncertainty ◽  
Epistemic Uncertainty ◽  
Fault Tree ◽  
Common Cause ◽  
Common Cause Failure ◽  
Engineered Systems ◽  
Model Parameter Uncertainty ◽  
Evidential Network

The fault tree analysis has been extensively implemented in failure analysis of engineered systems. In most cases, the probabilities of basic events, e.g. components’ failures, are represented by crisp values in the fault tree analyses. However, due to lack of knowledge, scarcity of failure data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models of components/systems, and such model parameter uncertainty can be quantified by the epistemic uncertainty. In addition, the common cause failure, related to the simultaneous failures of two or more components caused by physical interactions or shared environments, often exists in advanced engineered systems and computing systems. In this paper, by considering both the common cause failure and the epistemic uncertainty associated with model parameters, an evidential network model embedded with common cause failure is proposed to facilitate system failure analysis. The detailed transformations from some logic gates of a fault tree to an evidential network model are given. Moreover, the conditional belief mass tables are constructed to quantify the dependency between the states of components and the entire system. An engineering case of an aero-engine oil system, together with comparative results, is presented to demonstrate the effectiveness of the proposed evidential network model.

scholarly journals Assessment of the Common Cause Failure(CCFs) effect on Safety Instrumented System(SIS) by usign the Fault Tree Analysis (FTA) method

2020 ◽  
Vol 5 (2) ◽  
pp. 118-129
Author(s):  
Hassina Metatla ◽  
Mounira Rouainia
Keyword(s):  
Factor Model ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
International Electrotechnical Commission ◽  
Tree Analysis ◽  
Common Cause ◽  
Common Cause Failure ◽  
Common Cause Failures ◽  
The Common ◽  
Negative Impacts

The reliability of the safety-instrumented system (SIS) has received a lot of attention during the past decade, with the emergence of the new standards such as International Electrotechnical Commission IEC61508, and IEC61511, that provides a general framework for the design and implementation of these safety barriers. Among the problems influencing on the global SIS reliability: Common Cause Failure (CCF), which contributes too many accidents, that has a negative impacts, so it must be considered in the risk and reliability assessment for these systems. The aim of this work is to focus on the effects of common cause failures (CCFs) on the reliability of a SIS, by implementing a comparative SIS dependability study with and without consideration the CCFs, using the beta factor model, and the fault tree analysis (FTA) method.

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