Implementing dynamic flowgraph methodology models with logic programs

Ilkka Karanta (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

The dynamic flowgraph methodology is a promising way to find the prime implicants of a top event for a dynamic system possibly containing digital subsystems. This article demonstrates how to express dynamic flowgraph methodology models as logic programs, and top events as queries to those programs, in a natural and comprehensible way. Computation of the logic program lists the prime implicants of a top event in the system. We also present and implement an algorithm for computing the probability of the top event from its prime implicants. Together, computation of prime implicants and calculation of top event probability from these constitute a complete way of finding a system’s failure probability. Logic programs, implemented in this article in the leading logic programming language Prolog, enable rapid prototyping of dynamic flowgraph methodology models. The logic programming framework introduced here could also be utilized in teaching dynamic flowgraph methodology in risk analysis courses.
Original languageEnglish
Pages (from-to)302-314
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Volume227
Issue number3
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

Fingerprint

Logic programming
Rapid prototyping
Risk analysis
Computer programming languages
Teaching
Dynamical systems

Keywords

  • dynamic flowgraph methodology
  • logic programming
  • Prolog
  • dynamic probabilistic risk analysis

Cite this

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abstract = "The dynamic flowgraph methodology is a promising way to find the prime implicants of a top event for a dynamic system possibly containing digital subsystems. This article demonstrates how to express dynamic flowgraph methodology models as logic programs, and top events as queries to those programs, in a natural and comprehensible way. Computation of the logic program lists the prime implicants of a top event in the system. We also present and implement an algorithm for computing the probability of the top event from its prime implicants. Together, computation of prime implicants and calculation of top event probability from these constitute a complete way of finding a system’s failure probability. Logic programs, implemented in this article in the leading logic programming language Prolog, enable rapid prototyping of dynamic flowgraph methodology models. The logic programming framework introduced here could also be utilized in teaching dynamic flowgraph methodology in risk analysis courses.",
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AB - The dynamic flowgraph methodology is a promising way to find the prime implicants of a top event for a dynamic system possibly containing digital subsystems. This article demonstrates how to express dynamic flowgraph methodology models as logic programs, and top events as queries to those programs, in a natural and comprehensible way. Computation of the logic program lists the prime implicants of a top event in the system. We also present and implement an algorithm for computing the probability of the top event from its prime implicants. Together, computation of prime implicants and calculation of top event probability from these constitute a complete way of finding a system’s failure probability. Logic programs, implemented in this article in the leading logic programming language Prolog, enable rapid prototyping of dynamic flowgraph methodology models. The logic programming framework introduced here could also be utilized in teaching dynamic flowgraph methodology in risk analysis courses.

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