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Proceedings of the First Symposium on Aviation Maintenance and Management-Volume II pp 87–94Cite as

Bayesian Network Technology to Analyze Fault Trees

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 297))

Abstract

Analyzing a fault tree using Bayesian network (BN) technology has gotten lots of attention, in which a fault tree is mapped into an equivalent BN. Combining three kinds of problems in BNs, a systematic BN method used to analyze fault tree is raised and the corresponding algorithm flowchart is designed. With the new method, a fault tree for the main landing gear of a certain aircraft is analyzed and the result shows that more useful and accurate information can be achieved through this new method.

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References

  1. Portinale L, Bobbio A (1999) Bayesian networks for dependability analysis: an application to digital control reliability. In: Proceedings of the 15th conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, pp 551–558

    Google Scholar 

  2. Bobbio A, Portinale L, Minichino M et al (2001) Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. J Reliab Eng Syst Safe 71:249–260

    Article  Google Scholar 

  3. Zhang LW, Guo HP (2006) An introduction to Bayesian network. Science Press, Beijing (in Chinese)

    Google Scholar 

  4. Darwiche A (2009) Modeling and reasoning with Bayesian networks. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  5. Deng Q (2009) Safety system engineering. Northwest Industrial University Press, Xi’an (in Chinese)

    Google Scholar 

  6. Zhang NL (1998) Computational properties of two exact algorithms for Bayesian networks. J Appl Intell 9:173–183

    Article  Google Scholar 

  7. Pearl J (1986) Fusion, propagation, and structuring in belief networks. J Appl Intell 29:241–288

    MATH  MathSciNet  Google Scholar 

  8. Shenoy PP (1997) Binary join trees for computing marginals in the Shenoy-Shafer architecture. J Int J Approx Reason 17:239–263

    Article  MATH  MathSciNet  Google Scholar 

  9. Cooper GF (1990) The computational complexity of probabilistic inference using Bayesian belief networks. J Appl Intell 42:393–405

    MATH  Google Scholar 

  10. Shimony SE (1994) Finding MAPs for belief networks is NP-hard. J Appl Intell 68:399–410

    MATH  Google Scholar 

  11. Abdelbar AM, Hedetniemi SM (1998) Approximating MAPs for belief networks is NP-hard and other theorems. J Appl Intell 102:21–38

    MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Aeronautics Building A517, School of Aeronautics, Northwestern Polytechnical University, Laodong Road, Xi’an, 710072, Shaanxi, China

    Yao Wang & Qin Sun

Authors
  1. Yao Wang
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  2. Qin Sun
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Corresponding author

Correspondence to Yao Wang .

Editor information

Editors and Affiliations

  1. Northwestern Polytechnical University, Xi'an, China

    Jinsong Wang

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© 2014 Springer-Verlag Berlin Heidelberg

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Cite this paper

Wang, Y., Sun, Q. (2014). Bayesian Network Technology to Analyze Fault Trees. In: Wang, J. (eds) Proceedings of the First Symposium on Aviation Maintenance and Management-Volume II. Lecture Notes in Electrical Engineering, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54233-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-54233-6_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54232-9

  • Online ISBN: 978-3-642-54233-6

  • eBook Packages: EngineeringEngineering (R0)

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