Bayesian networks based rare event prediction with sensor data

Cheon, Seong-Pyo; Kim, Sungshin; Lee, So‐Young; Lee, Chong-Bum

doi:10.1016/j.knosys.2009.02.004

Cited by 44 publications

(23 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conversely, with the rapid establishment of advanced computer technology, it has attracted large interest in various sectors of science and engineering (Weber et al 2012). The attractiveness of this approach in the field of risk assessment can be attributed to the capabilities of dealing with very low-probability events (Hanea and Ale 2009), modelling elaborate networks of dependencies (as those characterizing complex systems) (Khakzad et al 2013) and, most of all, integrating information of different nature, from experimental data to expert judgements (Cheon et al 2009;Kim et al 2006). All these aspects make BNs particularly attractive in the study of natural hazards and their interaction with technological installations (Straub 2005;Bayraktarli et al 2005;Tolo et al 2014).…”

Section: Bayesian Networkmentioning

confidence: 99%

Robust vulnerability analysis of nuclear facilities subject to external hazards

Tolo

Patelli

Beer

2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in intuitive frameworks suitable for an interdisciplinary field such as that of risk analysis. This study proposes a novel approach for the quantification of risk exposure of nuclear facilities subject to extreme natural events. A Bayesian Network model, initially developed for the quantification of the risk of exposure from spent nuclear material stored in facilities subject to flooding hazards, is adapted and enhanced to include in the analysis the quantification of the uncertainty affecting the output due to the imprecision of data available and the aleatory nature of the variables involved. The model is applied to the analysis of the nuclear power station of Sizewell B in East Anglia (UK), through the use of a novel computational tool. The network proposed models the direct effect of extreme weather conditions on the facility along several time scenarios considering climate change predictions as well as the indirect effects of external hazards on the internal subsystems and the occurrence of human error. The main novelty of the study consists of the fully computational integration of Bayesian Networks with advanced Structural Reliability Methods, which allows to adequately represent both aleatory and epistemic aspects of the uncertainty affecting the input through the use of probabilistic models, intervals, imprecise random variables as well as probability bounds. The uncertainty affecting the output is quantified in order to attest the significance of the results and provide a complete and effective tool for riskinformed decision making.

show abstract

Section: Bayesian Networkmentioning

confidence: 99%

Robust vulnerability analysis of nuclear facilities subject to external hazards

Tolo

Patelli

Beer

2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…As proposed by Cheon et al (2009) in order to quantify the conditional probabilities of nodes, first data is gathered (Fig. 5).…”

Section: Model Quantificationmentioning

confidence: 99%

Industrial system knowledge formalization to aid decision making in maintenance strategies assessment

Medina-Oliva

Weber

Iung

2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

“…This is a very important research topic in artificial intelligence and decision support area (Liu et al, 2009;Cheon et al, 2009;Correa et al, 2009). It consists of a set of nodes and directed arcs.…”

Section: Bayesian Network Modelmentioning

confidence: 99%