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Takashi Takata - One of the best experts on this subject based on the ideXlab platform.
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quantitative Common Cause Failure modeling for auxiliary feedwater system involving the seismic induced degradation of flood barriers
Journal of Nuclear Science and Technology, 2014Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Flood barriers are important defenses which will reduce the internal flood-induced Failure risk of safety-related equipment in the turbine building. Contrarily, the degradation of flood barriers will increase the risk of internal flood-induced Common Cause Failure (CCF). Two layouts of auxiliary feedwater pumps system are compared to demonstrate the quantitative risk assessment of the possible degradation of flood barriers. The alpha decomposition method has been developed by the authors in order to quantitatively evaluate the CCF parameters based on the causal inference. Occurrence frequency and CCF triggering ability are two important elements which will decide the CCF risk significance of potential Common Causes. The seismic-induced internal flood combining with the degradation of flood barriers is analyzed. The degradation of flood barriers is treated as a stochastic process and a Markov model is applied to consider the time-dependent states. The Failure time of three auxiliary feedwater pumps is calc...
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α decomposition for estimating parameters in Common Cause Failure modeling based on causal inference
Reliability Engineering & System Safety, 2013Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Abstract The traditional α -factor model has focused on the occurrence frequencies of Common Cause Failure (CCF) events. Global α -factors in the α -factor model are defined as fractions of Failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available Failure data. Joint distributions of CCF parameters are actually determined by a set of possible Causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α -decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different Causes, which are named as decomposed α -factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential Causes and Failures. Secondly, beCause all potential Causes have different occurrence frequencies and abilities to trigger dependent Failures or independent Failures, a regression model is provided and proved by conditional probability. Global α -factors are expressed by explanatory variables (Causes’ occurrence frequencies) and parameters (decomposed α -factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α -decomposition process. This study shows that the α -decomposition method can integrate Failure information from Cause, component and system level. It can parameterize the CCF risk significance of possible Causes and can update probability distributions of global α -factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding Causes’ occurrence frequency of each targeted system.
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probabilistic Common Cause Failure modeling for auxiliary feedwater system after the introduction of flood barriers
Journal of Nuclear Science and Technology, 2013Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Causal inference is capable of assessing Common Cause Failure (CCF) events from the viewpoint of Causes’ risk significance. Authors proposed the alpha decomposition method for probabilistic CCF analysis, in which the classical alpha factor model and causal inference are integrated to conduct a quantitative assessment of Causes’ CCF risk significance. The alpha decomposition method includes a hybrid Bayesian network for revealing the relationship between component Failures and potential Causes, and a regression model in which CCF parameters (global alpha factors) are expressed by explanatory variables (Causes’ occurrence frequencies) and parameters (decomposed alpha factors). This article applies this method and associated databases needed to predict CCF parameters of auxiliary feedwater (AFW) system when defense barriers against internal flood are introduced. There is scarce operation data for functionally modified safety systems and the utilization of generic CCF databases is of unknown uncertainty. The ...
Jussi K Vaurio - One of the best experts on this subject based on the ideXlab platform.
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Common Cause Failure modeling
Encyclopedia of Quantitative Risk Analysis and Assessment, 2008Co-Authors: Jussi K VaurioAbstract:Modeling and quantification of simultaneous Failures of multiple components due to Common Causes is a frequent and often crucial task in safety system reliability and risk assessments. Several models of Common Cause Failures (CCF) are introduced. Their mutual relationships, limitations, and advantages, as well as sources of parametric values, are described. The models are presented in terms of basic multiple-Failure rates and probabilities that are independent of system testing schemes and intervals, and have direct connection to the basic event probabilities needed in system logic models. The early CCF models such as the β factor, binomial Failure rate, Common load, and distributed Failure probability models are all special cases of stochastic reliability analysis. Even if mathematically attractive, these models are inherently constrained by certain mutual couplings between the CCF rates or CCF probabilities. More general α factor and multiple Greek letter models are based on the ratios of multiple-Failure rates or probabilities. In applications, both models need some additional Failure rate from outside of the model to yield CCF rates and basic event probabilities. Extensive national and international Common Cause Failure data collection efforts are making it feasible to estimate plant-specific multiple-Failure rates and probabilities by combining data from many similar systems with empirically oriented Bayesian methods without assuming Common generic parametric values. This methodology is briefly described. It takes into account both statistical and assessment uncertainties due to limited observations, interpretations, and documentation of CCF events. Keywords: α factor; Bayesian methods; β factor; binomial-Failure rate; Common load; distributed-Failure probability; estimation; fault tree; Greek letter; impact vector; mapping; stochastic reliability
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consistent mapping of Common Cause Failure rates and alpha factors
Reliability Engineering & System Safety, 2007Co-Authors: Jussi K VaurioAbstract:The problem addressed is how to combine event experience data from multiple source plants to estimate Common Cause Failure (CCF) rates for a target plant. Alternative models are considered for transforming CCF parameters from systems with different numbers of similar components to obtain CCF-rates for a specific group of components. Two sets of rules are reviewed and compared for transforming rates and assessment uncertainties from larger to smaller systems, i.e. mapping down. Mapping down equations are presented also for the alpha-factors and for the variances of CCF rates. Consistent rules are developed for mapping up CCF-rates and uncertainties from smaller to larger systems. These mapping up rules are not limited to a binomial CCF model. It is shown how consistency requirements set certain limits to possible parametric values. Empirical alpha factors are used to estimate robust mapping parameters, and mapping up equations are derived for alpha factors as well. An assessment uncertainty procedure is presented for treating incomplete or vague information when estimating CCF-rates. Numerical studies illustrate mapping rules and procedures. Recommendations are made for practical applications.
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uncertainties and quantification of Common Cause Failure rates and probabilities for system analyses
Reliability Engineering & System Safety, 2005Co-Authors: Jussi K VaurioAbstract:Simultaneous Failures of multiple components due to Common Causes at random times are modelled by constant multiple-Failure rates. A procedure is described for quantification of Common Cause Failure (CCF) basic event probabilities for system models using plant-specific and multiple-plant Failure-event data. Methodology is presented for estimating CCF-rates from event data contaminated with assessment uncertainties. Generalised impact vectors determine the moments for the rates of individual systems or plants. These moments determine the effective numbers of events and observation times to be input to a Bayesian formalism to obtain plant-specific posterior CCF-rates. The rates are used to determine plant-specific Common Cause event probabilities for the basic events of explicit fault tree models depending on test intervals, test schedules and repair policies. Three methods are presented to determine these probabilities such that the correct time-average system unavailability can be obtained with single fault tree quantification. Recommended numerical values are given and examples illustrate different aspects of the methodology.
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Common Cause Failure probabilities in standby safety system fault tree analysis with testing scheme and timing dependencies
Reliability Engineering & System Safety, 2003Co-Authors: Jussi K VaurioAbstract:Abstract Modelling and quantification of Common Cause Failures (CCFs) in redundant standby safety systems can be implemented by implicit or explicit fault tree techniques. Common Cause event probabilities are derived for both methods for systems with time-related CCFs modelled by general multiple Failure rates. The probabilities are determined so that the correct time-average risk can be obtained by a single computation. The impacts of test intervals and test staggering are included. Staggered testing is best with a certain extra-testing rule, although extra testing is not important for 1-out-of- n : G systems. An economic model provides insights into the impacts of various parameters: the optimal test interval increases with increasing redundancy and testing cost, and it decreases with increasing accident cost and initiating event rate. Staggered testing with extra tests allows for the longest optimal test intervals. A practical technique is outlined for incorporating assessment uncertainties in the estimation of multiple Failure rates based on data from many plants or systems.
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extensions of the uncertainty quantification of Common Cause Failure rates
Reliability Engineering & System Safety, 2002Co-Authors: Jussi K VaurioAbstract:Abstract Uncertainties in Common Cause event observation, documentation and interpretation are taken into account by conditional probabilities and generalized impact vector weights that separate single and double events of a specific multiplicity in a single observation. Distributions and moments of Common Cause Failure (CCF) rates of a system are obtained in terms of the weights by using probability generating functions, combining assessment uncertainties and statistical uncertainties. These results are then used to generate effective plant-specific input data to general empirical Bayes estimation methods to combine data from many plants. The posterior output yields CCF probabilities for standby safety system fault tree analysis or probabilistic safety assessments of a target plant.
Xiaoyu Zheng - One of the best experts on this subject based on the ideXlab platform.
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quantitative Common Cause Failure modeling for auxiliary feedwater system involving the seismic induced degradation of flood barriers
Journal of Nuclear Science and Technology, 2014Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Flood barriers are important defenses which will reduce the internal flood-induced Failure risk of safety-related equipment in the turbine building. Contrarily, the degradation of flood barriers will increase the risk of internal flood-induced Common Cause Failure (CCF). Two layouts of auxiliary feedwater pumps system are compared to demonstrate the quantitative risk assessment of the possible degradation of flood barriers. The alpha decomposition method has been developed by the authors in order to quantitatively evaluate the CCF parameters based on the causal inference. Occurrence frequency and CCF triggering ability are two important elements which will decide the CCF risk significance of potential Common Causes. The seismic-induced internal flood combining with the degradation of flood barriers is analyzed. The degradation of flood barriers is treated as a stochastic process and a Markov model is applied to consider the time-dependent states. The Failure time of three auxiliary feedwater pumps is calc...
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α decomposition for estimating parameters in Common Cause Failure modeling based on causal inference
Reliability Engineering & System Safety, 2013Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Abstract The traditional α -factor model has focused on the occurrence frequencies of Common Cause Failure (CCF) events. Global α -factors in the α -factor model are defined as fractions of Failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available Failure data. Joint distributions of CCF parameters are actually determined by a set of possible Causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α -decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different Causes, which are named as decomposed α -factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential Causes and Failures. Secondly, beCause all potential Causes have different occurrence frequencies and abilities to trigger dependent Failures or independent Failures, a regression model is provided and proved by conditional probability. Global α -factors are expressed by explanatory variables (Causes’ occurrence frequencies) and parameters (decomposed α -factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α -decomposition process. This study shows that the α -decomposition method can integrate Failure information from Cause, component and system level. It can parameterize the CCF risk significance of possible Causes and can update probability distributions of global α -factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding Causes’ occurrence frequency of each targeted system.
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probabilistic Common Cause Failure modeling for auxiliary feedwater system after the introduction of flood barriers
Journal of Nuclear Science and Technology, 2013Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Causal inference is capable of assessing Common Cause Failure (CCF) events from the viewpoint of Causes’ risk significance. Authors proposed the alpha decomposition method for probabilistic CCF analysis, in which the classical alpha factor model and causal inference are integrated to conduct a quantitative assessment of Causes’ CCF risk significance. The alpha decomposition method includes a hybrid Bayesian network for revealing the relationship between component Failures and potential Causes, and a regression model in which CCF parameters (global alpha factors) are expressed by explanatory variables (Causes’ occurrence frequencies) and parameters (decomposed alpha factors). This article applies this method and associated databases needed to predict CCF parameters of auxiliary feedwater (AFW) system when defense barriers against internal flood are introduced. There is scarce operation data for functionally modified safety systems and the utilization of generic CCF databases is of unknown uncertainty. The ...
Akira Yamaguchi - One of the best experts on this subject based on the ideXlab platform.
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quantitative Common Cause Failure modeling for auxiliary feedwater system involving the seismic induced degradation of flood barriers
Journal of Nuclear Science and Technology, 2014Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Flood barriers are important defenses which will reduce the internal flood-induced Failure risk of safety-related equipment in the turbine building. Contrarily, the degradation of flood barriers will increase the risk of internal flood-induced Common Cause Failure (CCF). Two layouts of auxiliary feedwater pumps system are compared to demonstrate the quantitative risk assessment of the possible degradation of flood barriers. The alpha decomposition method has been developed by the authors in order to quantitatively evaluate the CCF parameters based on the causal inference. Occurrence frequency and CCF triggering ability are two important elements which will decide the CCF risk significance of potential Common Causes. The seismic-induced internal flood combining with the degradation of flood barriers is analyzed. The degradation of flood barriers is treated as a stochastic process and a Markov model is applied to consider the time-dependent states. The Failure time of three auxiliary feedwater pumps is calc...
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α decomposition for estimating parameters in Common Cause Failure modeling based on causal inference
Reliability Engineering & System Safety, 2013Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Abstract The traditional α -factor model has focused on the occurrence frequencies of Common Cause Failure (CCF) events. Global α -factors in the α -factor model are defined as fractions of Failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available Failure data. Joint distributions of CCF parameters are actually determined by a set of possible Causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α -decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different Causes, which are named as decomposed α -factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential Causes and Failures. Secondly, beCause all potential Causes have different occurrence frequencies and abilities to trigger dependent Failures or independent Failures, a regression model is provided and proved by conditional probability. Global α -factors are expressed by explanatory variables (Causes’ occurrence frequencies) and parameters (decomposed α -factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α -decomposition process. This study shows that the α -decomposition method can integrate Failure information from Cause, component and system level. It can parameterize the CCF risk significance of possible Causes and can update probability distributions of global α -factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding Causes’ occurrence frequency of each targeted system.
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probabilistic Common Cause Failure modeling for auxiliary feedwater system after the introduction of flood barriers
Journal of Nuclear Science and Technology, 2013Co-Authors: Xiaoyu Zheng, Akira Yamaguchi, Takashi TakataAbstract:Causal inference is capable of assessing Common Cause Failure (CCF) events from the viewpoint of Causes’ risk significance. Authors proposed the alpha decomposition method for probabilistic CCF analysis, in which the classical alpha factor model and causal inference are integrated to conduct a quantitative assessment of Causes’ CCF risk significance. The alpha decomposition method includes a hybrid Bayesian network for revealing the relationship between component Failures and potential Causes, and a regression model in which CCF parameters (global alpha factors) are expressed by explanatory variables (Causes’ occurrence frequencies) and parameters (decomposed alpha factors). This article applies this method and associated databases needed to predict CCF parameters of auxiliary feedwater (AFW) system when defense barriers against internal flood are introduced. There is scarce operation data for functionally modified safety systems and the utilization of generic CCF databases is of unknown uncertainty. The ...
Mohammed Hajeeh - One of the best experts on this subject based on the ideXlab platform.
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availability of deteriorated system with inspection subject to Common Cause Failure and human error
International Journal of Operational Research, 2011Co-Authors: Mohammed HajeehAbstract:In this paper, a Markovian model was used to derive a closed form expression for the steady state availability for a system that is allowed to undergo several stages of deteriorations subject to several Failures at each state. The Failures include random Failure, Common-Cause Failure and human error. The system is inspected after each deteriorated state, where one of the two actions is possible, no action or minimal repair which brings the system to the previous deteriorated state. A complete replacement is allowed after n deteriorated states. Moreover, the system is allowed to become as good as new after Common-Cause or human error Failures. The steady state availability is derived for the complete problem, and for three special cases. An example is provided to illustrate the performance of the different cases.
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reliability and availability of a standby system with Common Cause Failure
International Journal of Operational Research, 2011Co-Authors: Mohammed HajeehAbstract:The reliability and availability are considered for four series configurations with both warm and cold standby with the existence of Common Cause Failure of the system at all states. The mean time to Failure (MTTF) and steady state availability are derived for all configurations. It is assumed that the time between Failures and repair time to be exponentially distributed. Examples are presented for comparing all configurations for specific values of the different parameters under various costs of the components. The configurations are ranked based on preference with respect to different parameter ratios.