Dynamic system safety analysis in HiP-HOPS with Petri Nets and Bayesian Networks

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2018-06Keyword
Fault tree analysisReliability analysis
Model-based safety analysis
Dynamic fault trees
Temporal fault trees
HiP-HOPS
Petri Nets
Bayesian Networks
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© 2018 Elsevier Ltd. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.Peer-Reviewed
YesAccepted for publication
2018-02-01
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Show full item recordAbstract
Dynamic systems exhibit time-dependent behaviours and complex functional dependencies amongst their components. Therefore, to capture the full system failure behaviour, it is not enough to simply determine the consequences of different combinations of failure events: it is also necessary to understand the order in which they fail. Pandora temporal fault trees (TFTs) increase the expressive power of fault trees and allow modelling of sequence-dependent failure behaviour of systems. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. This in turn makes it less viable for use in modern, iterated system design processes, which requires a quicker turnaround and consistency across evolutions. In this paper, we propose for a model-based analysis of temporal fault trees via HiP-HOPS, which is a state-of-the-art model-based dependability analysis method supported by tools that largely automate analysis and optimisation of systems. The proposal extends HiP-HOPS with Pandora, Petri Nets and Bayesian Networks and results to dynamic dependability analysis that is more readily integrated into modern design processes. The effectiveness is demonstrated via application to an aircraft fuel distribution system.Version
Accepted manuscriptCitation
Kabir S, Walker M and Papadopoulos Y (2018) Dynamic system safety analysis in HiP-HOPS with Petri Nets and Bayesian Networks. Safety Science. 105: 55-70.Link to Version of Record
https://doi.org/10.1016/j.ssci.2018.02.001Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.ssci.2018.02.001