Solving dynamic flowgraph methodology models using binary decision diagrams

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)

Abstract

Dynamic flowgraph methodology (DFM) is a computationally challenging approach to the reliability analysis of dynamic systems with feedback loops. To improve the computational efficiency of DFM modelling, we propose a new approach, based on binary decision diagrams (BDDs), to solving DFM models. The objective of DFM analysis is to identify the root causes of a postulated top event. The result is a set of prime implicants that represent system faults resulting from diverse combinations of software logic errors, hardware failures, human errors and adverse environmental conditions. Two approaches to solving prime implicants have been implemented in software called YADRAT. The first approach is based on meta-products, and the second on zero-suppressed BDDs (ZBDD). Both approaches have been used previously in fault tree analysis. In this work, the ideas of prime implicant computations are adapted to a dynamic reliability analysis approach combined with multi-valued logic. The computational efforts required for the two approaches are compared by analysing three example systems. The results of the comparison show that BDDs are applicable in DFM computation and that in particular the ZBDD-based approach can solve moderately sized DFM models in a reasonable time.
Original languageEnglish
Pages (from-to)206-216
Number of pages11
JournalReliability Engineering and System Safety
Volume111
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

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Binary decision diagrams
Reliability analysis
Fault tree analysis
Computational efficiency
Dynamical systems
Feedback
Hardware

Keywords

  • Binary decision diagram
  • dynamic flowgraph methodology
  • dynamic reliability analysis
  • multi-valued logic
  • prime implicant

Cite this

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title = "Solving dynamic flowgraph methodology models using binary decision diagrams",
abstract = "Dynamic flowgraph methodology (DFM) is a computationally challenging approach to the reliability analysis of dynamic systems with feedback loops. To improve the computational efficiency of DFM modelling, we propose a new approach, based on binary decision diagrams (BDDs), to solving DFM models. The objective of DFM analysis is to identify the root causes of a postulated top event. The result is a set of prime implicants that represent system faults resulting from diverse combinations of software logic errors, hardware failures, human errors and adverse environmental conditions. Two approaches to solving prime implicants have been implemented in software called YADRAT. The first approach is based on meta-products, and the second on zero-suppressed BDDs (ZBDD). Both approaches have been used previously in fault tree analysis. In this work, the ideas of prime implicant computations are adapted to a dynamic reliability analysis approach combined with multi-valued logic. The computational efforts required for the two approaches are compared by analysing three example systems. The results of the comparison show that BDDs are applicable in DFM computation and that in particular the ZBDD-based approach can solve moderately sized DFM models in a reasonable time.",
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}

Solving dynamic flowgraph methodology models using binary decision diagrams. / Björkman, Kim.

In: Reliability Engineering and System Safety, Vol. 111, 2013, p. 206-216.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Solving dynamic flowgraph methodology models using binary decision diagrams

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PY - 2013

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N2 - Dynamic flowgraph methodology (DFM) is a computationally challenging approach to the reliability analysis of dynamic systems with feedback loops. To improve the computational efficiency of DFM modelling, we propose a new approach, based on binary decision diagrams (BDDs), to solving DFM models. The objective of DFM analysis is to identify the root causes of a postulated top event. The result is a set of prime implicants that represent system faults resulting from diverse combinations of software logic errors, hardware failures, human errors and adverse environmental conditions. Two approaches to solving prime implicants have been implemented in software called YADRAT. The first approach is based on meta-products, and the second on zero-suppressed BDDs (ZBDD). Both approaches have been used previously in fault tree analysis. In this work, the ideas of prime implicant computations are adapted to a dynamic reliability analysis approach combined with multi-valued logic. The computational efforts required for the two approaches are compared by analysing three example systems. The results of the comparison show that BDDs are applicable in DFM computation and that in particular the ZBDD-based approach can solve moderately sized DFM models in a reasonable time.

AB - Dynamic flowgraph methodology (DFM) is a computationally challenging approach to the reliability analysis of dynamic systems with feedback loops. To improve the computational efficiency of DFM modelling, we propose a new approach, based on binary decision diagrams (BDDs), to solving DFM models. The objective of DFM analysis is to identify the root causes of a postulated top event. The result is a set of prime implicants that represent system faults resulting from diverse combinations of software logic errors, hardware failures, human errors and adverse environmental conditions. Two approaches to solving prime implicants have been implemented in software called YADRAT. The first approach is based on meta-products, and the second on zero-suppressed BDDs (ZBDD). Both approaches have been used previously in fault tree analysis. In this work, the ideas of prime implicant computations are adapted to a dynamic reliability analysis approach combined with multi-valued logic. The computational efforts required for the two approaches are compared by analysing three example systems. The results of the comparison show that BDDs are applicable in DFM computation and that in particular the ZBDD-based approach can solve moderately sized DFM models in a reasonable time.

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