Bayesian Inference for Probabilistic Risk Assessment

Kelly, Dana; Smith, Curtis

doi:10.1007/978-1-84996-187-5

Cited by 79 publications

(71 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MCMC approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, especially in nuclear PRA [12,13]. Intractable problems of integrations (prior distributions and likelihood functions) have been solved with powerful software packages.…”

Section: Review Of the Alpha Decomposition Methods For Ccf Analysismentioning

confidence: 99%

Quantitative common cause failure modeling for auxiliary feedwater system involving the seismic-induced degradation of flood barriers

Zheng

Yamaguchi

Takata

2013

Journal of Nuclear Science and Technology

View full text Add to dashboard Cite

Flood barriers are important defenses which will reduce the internal flood-induced failure risk of safetyrelated 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 calculated based on the water flow rate through flood barriers. CCF triggering abilities of internal floods are calculated which are represented as decomposed alpha factors. This article shows the updating process of CCF parameters according to Bayesian inference and hypothetical databases. It is concluded that the issue of CCF modeling is not only decided by the number of redundant components but also decided by causes and plant-specific design.

show abstract

Section: Review Of the Alpha Decomposition Methods For Ccf Analysismentioning

confidence: 99%

Quantitative common cause failure modeling for auxiliary feedwater system involving the seismic-induced degradation of flood barriers

Zheng

Yamaguchi

Takata

2013

Journal of Nuclear Science and Technology

View full text Add to dashboard Cite

show abstract

“…These studies emphasize the potential failures caused by the abnormal conditions such as accidents, natural disasters, and subsystem failures [35]. In this sense, the ordinary PRA separately models the probabilities of abnormal conditions and conditional failures and analyzes them in combination in a Bayesian probability network [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Propagation Through a Platform: The Failure Risk Perspective on Platform Sharing

Kang

Hong

Huh

et al. 2015

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

A product platform is a set of subsystems and interfaces that are commonly shared by a variety of products. Although platform sharing is considered an effective means of cost saving, it also runs the risk of propagating a particular failure across multiple products when the platform is defectively designed. Thus, sharing a common platform effectively can amplify the risk of incurring a large number of failures. In this paper, we formulate a quantitative model for assessing amplified failure risk. Our analysis shows an unexpected result that the platform risk increases as we become more assured of our design capability; i.e., when a defective design rarely happens, the magnitude of risk amplification becomes much larger. The numerical investigation of a platform planning case sheds light on the importance of risk assessment in determining the level of commonality in designing a platform.Index Terms-Decisions under risk and uncertainty, decision analysis, mathematical tools for management of technology, new product development process, R&D management.

show abstract

“…In other words, the probabilities predicted based on previous observations can be corrected according to the results of new information and observations (Jebb 2017; URL-1 2017). Bayes' theorem modifies a prior probability, yielding a posterior probability, via the Equation 1 (Kelly and Smith 2011).…”

Section: Bayesian Networkmentioning

confidence: 99%

Dinamik Çığ Tehlike Değerlendirmesi İçin Bayes Ağlarının CBS'ye Entegrasyonu: UKVA Perspektifi

Yılmaz

Öztürk

2018

Doğal Afetler Ve Çevre Dergisi

View full text Add to dashboard Cite

Natural hazard assessments are core to risk definition and early warning systems and play a fundamental role in the prevention of major damages. Traditional hazard identification methods are static. For this reason, new information and conditions cannot be easily included in the pre-defined hazard assessments. The Bayesian Networks can be used effectively for dynamic hazard identification. In this study, a methodology based on the Bayesian Networks model is presented for dynamic avalanche hazard assessment, in which changed and renewed data can be included in the system. In the proposed methodology, the integration of the Bayesian Networks and Geographical Information Systems (GIS) is modeled in the National Spatial Data Infrastructure (NSDI) perspective. In this structure, it is possible to combine and analyze the data obtained from different sources and factors for avalanche hazard can be dynamically updated with real-time updated data and temporal hazard mapping can be produced. The proposed methodology provides a generic structure and has an attribute making it applicable for dynamic mapping studies for other disasters.

show abstract

Bayesian Inference for Probabilistic Risk Assessment

Cited by 79 publications

References 0 publications

Quantitative common cause failure modeling for auxiliary feedwater system involving the seismic-induced degradation of flood barriers

Quantitative common cause failure modeling for auxiliary feedwater system involving the seismic-induced degradation of flood barriers

Risk Propagation Through a Platform: The Failure Risk Perspective on Platform Sharing

Dinamik Çığ Tehlike Değerlendirmesi İçin Bayes Ağlarının CBS'ye Entegrasyonu: UKVA Perspektifi

Contact Info

Product

Resources

About