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

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.

Dependability ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 3-9 ◽  
Author(s):  
A. V. Antonov ◽  
E. Yu. Galivets ◽  
V. A. Chepurko ◽  
A. N. Cherniaev

Aim. This paper is the continuation of [1] that proposes using the R programming language for fault tree analysis (FTA). In [1], three examples are examined: fault tree (FT) calculation per known probabilities, dynamic FT calculation per known distributions of times to failure for a system’selements. In the latter example, FTA is performed for systems with elements that are described by different functional and service models. Fault tree analysis (FTA) is one of the primary methods of dependability analysis of complex technical systems. This process often utilizes commercial software tools like Saphire, Risk Spectrum, PTC Windchill Quality, Arbitr, etc. Practically each software tool allows calculating the dependability of complex systems subject to possible common cause failures (CCF). CCF are the associated failures of a group of several elements that occur simultaneously or within a short time interval (i.e. almost simultaneously) due to one common cause (e.g. a sudden change in the climatic service conditions, flooding of the premises, etc.). An associated failure is a multiple failure of several system elements, of which the probability cannot be expressed simply as the product of the probabilities of unconditional failures of individual elements. There are several generally accepted models used in CCF probability calculation: the Greek letters model, the alpha, beta factor models, as well as their variations. The beta factor model is the most simple in terms of associated failures simulation and further dependability calculation. The other models involve combinatorial search associated events in a group of n events, that becomes labor-consuming if the number n is large. Therefore, in the above software tools there are some restrictions on the n, beyond which the probability of CCF is calculated approximately. In the current R FaultTree package version there are no above CCF models, therefore all associated failures have to be simulated manually, which is not complicated if the number of associated events is small, as well as useful in terms of understanding the various CCF models. In this paper, for the selected diagram a detailed analysis of the procedure of associated failures simulation is performed for alpha and beta factor models. The Purposeof this paper consists in the detailed analysis of the alpha and beta factor methods for a certain diagram, in the demonstration of fault tree creation procedure taking account of ССF using R’s FaultTree package. Methods. R’s FaultTree scripts were used for the calculations and FTA capabilities demonstration.Conclusions. Two examples are examined in detail. In the first example, for the selected block diagram that contains two groups of elements subject to associated failures, the alpha factor model is applied. In the second example, the beta factor model is applied. The deficiencies of the current version of FaultTree package are identified. Among the main drawbacks we should indicate the absence of some basic logical gates.


Author(s):  
MARY ANN LUNDTEIGEN ◽  
MARVIN RAUSAND

This article presents a practical approach to reliability assessment of a complex safety instrumented system that is susceptible to common cause failures. The approach is based on fault tree analysis where the common cause failures are included by post-processing the minimal cut sets. The approach is illustrated by a case study of a safety instrumented function of a workover control system that is used during maintenance interventions into subsea oil and gas wells. The case study shows that the approach is well suited for identifying potential failures in complex systems and for including design engineers in the verification of the reliability analyses. Unlike many software tools for fault tree analysis, the approach gives conservative estimates for reliability. The suggested approach represents a useful extension to current reliability analysis methods.


Author(s):  
Min Zhang ◽  
Zhijian Zhang ◽  
Ali Mosleh ◽  
Sijuan Chen

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.


Author(s):  
Tao Feng ◽  
Rongxing Duan ◽  
Yanni Lin ◽  
Yining Zeng

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.


2017 ◽  
Vol 2 (4) ◽  
pp. 199-206
Author(s):  
Mourad CHEBILA ◽  
Fares INNAL

Dependability of multi-component systems is highly impacted by common cause failures, what necessitates the appropriate consideration of such events in the dependability modeling process. This paper is dedicated to study the application of the binomial failure rate model in handling the contribution of common cause failures to estimate two key dependability indicators, namely: unavailability and unconditional failure intensity, using fault tree analysis with the probabilistic treatment of the associated parameter uncertainty. The results of such application are thoroughly compared to those of the traditional Beta factor model to highlight the possible differences.


Author(s):  
Geng Feng

The importance of reliability to complex systems cannot be disputed as they are the backbones of our society. In practice, the common cause failures may have severe reverse function on complex systems’ overall stability. Survival Signature opens a new way to perform reliability analysis on systems with multiple component types. This paper under takes a research on survival signature-based reliability analysis on complex systems susceptible to Common Cause Failures. To be specific, it proposes the standard [Formula: see text]-factor model and general [Formula: see text]-factor model to combine with the survival signature. In practical applications, the [Formula: see text]-factor estimator of the system might not be defined completely due to limited data, or knowledge which requires to take imprecision into account. Some numerical cases are presented to show the applicability of the methods for complex systems. In addition, this paper may attract people’s attention on the conception of Design for Reliability.


Author(s):  
ZHIJIE PAN ◽  
YASUO NONAKA

This paper presents a new concept, complex common cause failure, for common cause failure analysis. A common stress model is developed, in which the common cause events are described as common stresses that will affect directly and simultaneously on the internal aging process of each system component and further change their failure probabilities, while the internal aging processes of components are still considered mutually independent. The common stress model can be used to estimate the reliability of systems with complex common cause failures.


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