International audienceFault Trees or Bow Tie Diagrams are widely used for system dependability assessment. Some probabilistic extensions have been proposed by using Bayesian network formalism. This article proposes a general modeling approach under the form of a probabilistic relational model (PRM), relational extension of Bayesian networks, that can represent any fault tree, defined as an event tree with possible safety barriers, simply described in a relational database. We first describe an underlying relational schema describing a generic fault tree, and the probabilistic dependencies needed to model the existence of an event given the possible existence of its related causes and eventual safety barriers
For many physical systems (e.g., computer systems, computer networks, industrial plants, etc.) one o...
In this paper, we present an approach to reliability modeling and analysis based on the automatic c...
Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but desp...
Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They ha...
Probabilistic Model Checking is an established technique used in the dependability analysis of safet...
Cyber-physical systems have increasingly intricate architectures and failure modes, which is due to ...
Fault trees show which joint components' faults mean system faults. Fault trees can often be used to...
This paper presents an extension of Bayesian networks (BN) applied to reliability analysis. We devel...
Bayesian Networks (BN) have in previous literature been recognized as a powerful tool for safety ana...
Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They ha...
Abstract: Fault trees show which joint components ' faults mean system faults. Fault trees can ...
Probabilistic graphical model representations of relational data provide a number of desired feature...
AbstractIn this paper, we present an approach to reliability modeling and analysis based on the auto...
Fault Trees (FT) are one of the most popular techniques for dependability analysis of large, safety ...
In recent years, several approaches to generate probabilistic counterexamples have been proposed. Th...
For many physical systems (e.g., computer systems, computer networks, industrial plants, etc.) one o...
In this paper, we present an approach to reliability modeling and analysis based on the automatic c...
Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but desp...
Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They ha...
Probabilistic Model Checking is an established technique used in the dependability analysis of safet...
Cyber-physical systems have increasingly intricate architectures and failure modes, which is due to ...
Fault trees show which joint components' faults mean system faults. Fault trees can often be used to...
This paper presents an extension of Bayesian networks (BN) applied to reliability analysis. We devel...
Bayesian Networks (BN) have in previous literature been recognized as a powerful tool for safety ana...
Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They ha...
Abstract: Fault trees show which joint components ' faults mean system faults. Fault trees can ...
Probabilistic graphical model representations of relational data provide a number of desired feature...
AbstractIn this paper, we present an approach to reliability modeling and analysis based on the auto...
Fault Trees (FT) are one of the most popular techniques for dependability analysis of large, safety ...
In recent years, several approaches to generate probabilistic counterexamples have been proposed. Th...
For many physical systems (e.g., computer systems, computer networks, industrial plants, etc.) one o...
In this paper, we present an approach to reliability modeling and analysis based on the automatic c...
Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but desp...