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Title: | Improved efficiency in qualitative fault tree analysis |
Authors: | Sinnamon, Roslyn M. Andrews, J.D. |
Keywords: | Fault tree analyses Binary decision diagrams Minimal cut sets |
Issue Date: | 1997 |
Publisher: | © John Wiley & Sons |
Citation: | SINNAMIN, R.M. and ANDREWS, J.D., 1997. Improved efficiency in qualitative fault tree analysis. Quality and Reliability Engineering International, 13 (5), pp 293-298 |
Abstract: | The fault tree diagram itself is an excellent way of deriving the failure logic for a system and
representing it in a form which is ideal for communication to managers, designers, operators, etc. Since
the method was first conceived, algorithms to derive the minimal cut sets have worked directly with
the fault tree diagram using either bottom-up or top-down approaches. These conventional techniques
have several disadvantages when it comes to analysing the fault tree. For complex systems an analysis
may produce hundreds of thousands of minimal cut sets, the determination of which can be a very
time-consuming process. Also, for large fault trees it may not be possible to evaluate all minimal cut
sets, so methods to identify those event combinations which provide the most significant contributions
to the system failure are evoked. Such methods include probabilistic or order culling to reduce the
problem to a practical size, but they can also create considerable inaccuracies when it comes to
evaluating top event probability parameters.
This paper describes how the binary decision diagram method can be employed to evaluate the
minimal cut sets of a fault tree efficiently and without the need to use approximations such as order
culling. |
Description: | This article is Restricted Access. It was published in the journal, Quality and Reliability Engineering International [© John Wiley & Sons] and is also available at: http://www3.interscience.wiley.com/journal/3680/home |
URI: | https://dspace.lboro.ac.uk/2134/3671 |
ISSN: | 0748-8017 |
Appears in Collections: | Closed Access (Aeronautical and Automotive Engineering)
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