Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Bayesian networks for dependability analysis: an application to digital control reliability
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Parametric dependability analysis through probabilistic Horn abduction
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
An ontology-based approach for constructing Bayesian networks
Data & Knowledge Engineering
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
Engineering Applications of Artificial Intelligence
A dynamic Bayesian network based framework to evaluate cascading effects in a power grid
Engineering Applications of Artificial Intelligence
An optimization-based approach for the design of Bayesian networks
Mathematical and Computer Modelling: An International Journal
A2thOS: availability analysis and optimisation in SLAs
International Journal of Network Management
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Journal of Systems and Software
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This paper presents an extension of Bayesian networks (BN) applied to reliability analysis. We developed a general methodology for reliability modeling of complex systems based on Bayesian networks. A reliability structure represented as a reliability block diagram is transformed to a Bayesian network representation, and with this, the reliability of the system can be obtained using probability propagation techniques. This allows for modeling complex systems, such as a bridge type, and dependencies between failures, which are difficult to obtain with conventional reliability analysis techniques. The relation between a BN and fault tree, and some advantages of BN for modeling system reliability are shown. We present some examples of the application of this methodology in solving difficult cases, which occur in reliability analysis of power plants.