This paper presents a method for analysing fault trees that contain independent sets of mutually exclusive (disjoint) events of different cardinality. Disjoint events can be used to model several issues, e.g. multi-state systems with multistate components, different attack alternatives in se-curity related studies, and components in phased mission systems. Basic events of coherent and non coherent binary trees can be considered as belonging to sets of cardinality 2. Each event is associated with a binary variable, and a labelling technique is used to distinguish the variables belonging to different sets. The proposed analysis method is based on the approach of Binary Decision Diagrams (BDD). The application of the rules for the construction...
Binary Decision Diagram (BDD) based fault tree analysis algorithms are among the most efficient ones...
Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Sin...
Quantitative analysis of a non-coherent fault tree structure using binary decision diagram
The paper deals with the analysis of fault trees containing mutually exclusive (disjoint) events. A ...
Risk and safety assessments performed on potentially hazardous industrial systems commonly utilize f...
The use of Binary Decision Diagrams (BDDs) in fault tree analysis provides both an accurate and effi...
Fault Tree Analysis (FTA) is widely used for safety and more recently, for security studies. Dependi...
The aim of th1s thesis is to develop the Binary Decision Diagram method for the analysis of coherent...
Non coherent fault trees characterised by non monotonic structure functions allows easier modelling ...
With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis a significant ...
Fault Tree Analysis is now a widely accepted technique to assess the probability and frequency of sy...
Fault trees show which joint components' faults mean system faults. Fault trees can often be used to...
The fault tree diagram defines the causes of the system failure mode or ‘top event’ in terms of the ...
AbstractBinary Decision Diagram (BDD) based fault tree analysis algorithms are among the most effici...
The use of Binary Decision Diagrams(BDDs)in fault tree analysis provides an exact and efficient mean...
Binary Decision Diagram (BDD) based fault tree analysis algorithms are among the most efficient ones...
Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Sin...
Quantitative analysis of a non-coherent fault tree structure using binary decision diagram
The paper deals with the analysis of fault trees containing mutually exclusive (disjoint) events. A ...
Risk and safety assessments performed on potentially hazardous industrial systems commonly utilize f...
The use of Binary Decision Diagrams (BDDs) in fault tree analysis provides both an accurate and effi...
Fault Tree Analysis (FTA) is widely used for safety and more recently, for security studies. Dependi...
The aim of th1s thesis is to develop the Binary Decision Diagram method for the analysis of coherent...
Non coherent fault trees characterised by non monotonic structure functions allows easier modelling ...
With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis a significant ...
Fault Tree Analysis is now a widely accepted technique to assess the probability and frequency of sy...
Fault trees show which joint components' faults mean system faults. Fault trees can often be used to...
The fault tree diagram defines the causes of the system failure mode or ‘top event’ in terms of the ...
AbstractBinary Decision Diagram (BDD) based fault tree analysis algorithms are among the most effici...
The use of Binary Decision Diagrams(BDDs)in fault tree analysis provides an exact and efficient mean...
Binary Decision Diagram (BDD) based fault tree analysis algorithms are among the most efficient ones...
Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Sin...
Quantitative analysis of a non-coherent fault tree structure using binary decision diagram