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Qualitative analysis of complex modularized fault trees using binary decision diagrams

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journal contribution
posted on 2008-10-30, 10:00 authored by Rasa Remenyte, J.D. Andrews
Fault tree analysis is commonly used in the reliability assessment of industrial systems. When complex systems are studied conventional methods can become computationally intensive and require the use of approximations. This leads to inaccuracies in evaluating system reliability. To overcome such disadvantages, the binary decision diagram (BDD) method has been developed. This method improves accuracy and efficiency, because the exact solutions can be calculated without the requirement to calculate minimal cut sets as an intermediate phase. Minimal cut sets can be obtained if needed. BDDs are already proving to be of considerable use in system reliability analysis. However, the difficulty is with the conversion process of the fault tree to the BDD. The ordering of the basic events can have a crucial effect on the size of the final BDD, and previous research has failed to identify an optimum scheme for producing BDDs for all fault trees. This paper presents an extended strategy for the analysis of complex fault trees. The method utilizes simplification rules that are applied to the fault tree to reduce it to a series of smaller subtrees whose solution is equivalent to the original fault tree. The smaller subtree units are less sensitive to the basic event ordering during BDD conversion. BDDs are constructed for every subtree. Qualitative analysis is performed on the set of BDDs to obtain the minimal cut sets for the original top event. It is shown how to extract the minimal cut sets from complex and modular events in order to obtain the minimal cut sets of the original fault tree in terms of basic events.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Citation

REMENYTE, R. and ANDREWS, J.D., 2006. Qualitative analysis of complex modularized fault trees using binary decision diagrams. Proceedings of the Institution of Mechanical Engineers, Part O : Journal of Risk and Reliability, 220 (1), pp 45-53 [DOI: 10.1243/1748006XJRR10]

Publisher

© IMechE / Professional Engineering Publishing

Publication date

2006

Notes

This article was published in the journal, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability [© IMechE] and is also available at: http://journals.pepublishing.com/content/119859

ISSN

1748-006X;1748-0078

Language

  • en

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