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Failure Propagation Modeling for Safety Analysis using Causal Bayesian Networks
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
2013 (English)Report (Other academic)
Abstract [en]

Bayesian Networks (BN) have in previous literature been recognized as a powerful tool for safety analysis, with several advantages over traditional methods such as fault trees. The construction of BNs for safety analysis is however cumbersome; no easier than construction of fault trees. The paper therefore presents a systematic method for construction of BNs for analysis. It is recognized that a special kind of BNs is required, namely Causal BNs. The basic principle to construct these Causal BNs is to utilize specifications of services, or requirements, and their relationships. The approach is especially attractive in the context of safety standards (e.g. ISO26262) where specification and traceability of requirements is already mandatory. The framework in the paper also provides a theoretical link between requirements engineering and the dependability theoretical definitions of fault and failure.

Place, publisher, year, edition, pages
2013. , 19 p.
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:kth:diva-120597ISRN: KTH/MMK/R-13-05-SEOAI: oai:DiVA.org:kth-120597DiVA: diva2:615977
Available from: 2013-04-13 Created: 2013-04-13 Last updated: 2016-11-25

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf