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Bayesian Networks for Reliability Analysis of Complex Systems

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Progress in Artificial Intelligence — IBERAMIA 98 (IBERAMIA 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1484))

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Abstract

This paper presents an extension of Bayesian networks (BN) applied to reliability analysis. We developed a general methodology for reliability modeling of complex systems based on Bayesian networks. A reliability structure represented as a reliability block diagram is transformed to a Bayesian network representation, and with this, the reliability of the system can be obtained using probability propagation techniques. This allows for modeling complex systems, such as a bridge type, and dependencies between failures, which are difficult to obtain with conventional reliability analysis techniques. The relation between a BN and fault tree, and some advantages of BN for modeling system reliability are shown. We present some examples of the application of this methodology in solving difficult cases, which occur in reliability analysis of power plants.

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References

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Author information

Authors and Affiliations

  1. Unidad de Procesos Térmicos, Instituto de Investigaciones Eléctricas, Apdo.Postal 1-475, C.P. 62001, Cuernavaca, Morelos, México

    José Gerardo Torres-Toledano

  2. Departamento de Computación, ITESM - Campus Morelos., Apdo.Postal 99-C, C.P. 62050, Cuernavaca, Morelos, México

    Luis Enrique Sucar

Authors
  1. José Gerardo Torres-Toledano
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  2. Luis Enrique Sucar
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Editor information

Editors and Affiliations

  1. Dep. Informática, Fac. Ciências de Lisboa, Bloco C5, Piso 1, Campo Grande, 1700, Lisboa, Portugal

    Helder Coelho

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© 1998 Springer-Verlag Berlin Heidelberg

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Torres-Toledano, J.G., Sucar, L.E. (1998). Bayesian Networks for Reliability Analysis of Complex Systems. In: Coelho, H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science(), vol 1484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49795-1_17

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  • DOI: https://doi.org/10.1007/3-540-49795-1_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64992-2

  • Online ISBN: 978-3-540-49795-0

  • eBook Packages: Springer Book Archive

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