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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)In: 2013 2nd International Conference On Control And Fault-Tolerant Systems, IEEE , 2013, 91-97 p.Conference paper, Published paper (Refereed)
Abstract [en]

The method Bayesian Networks (BN) has, 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 safety analysis. It is recognized that a special kind of BNs is required, namely Causal BNs. The basic principle for constructing these Causal BNs is to utilize specifications of requirements, here viewed as services, 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
IEEE , 2013. 91-97 p.
Series
Conference on Control and Fault-Tolerant Systems, ISSN 2162-1209
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:kth:diva-142898ISI: 000331082600015Scopus ID: 2-s2.0-84897689631ISBN: 978-1-4799-2855-2 (print)OAI: oai:DiVA.org:kth-142898DiVA: diva2:705061
Conference
2nd International Conference on Control and Fault-Tolerant Systems (SysTol), OCT 09-11, 2013, Nice, France
Note

QC 20140314

Available from: 2014-03-14 Created: 2014-03-13 Last updated: 2014-03-14Bibliographically approved

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