Efficient reliability and uncertainty assessment on lifeline networks using the survival signature

Geng Feng, Sean Reed, Edoardo Patelli, Michael Beer, Frank P.A. Coolen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Lifeline networks, such as water distribution and transportation networks, are the backbone of our societies, and the study of their reliability of them is required. In this paper, a survival signature-based reliability analysis method is proposed to analyse the complex networks. It allows to consider all the characters of the network instead of just analysing the most critical path. What is more, the survival signature separates the system structure from its failure distributions, and it only needs to be calculated once, which makes it efficient to analyse complex networks. However, due to lack of data, there often exists imprecision within the network failure time distribution parameters and hence the survival signature. An efficient algorithm which bases on the reduced ordered binary decision diagrams (BDD) data structure for the computation of survival signatures is presented. Numerical example shows the applicability of the approaches.

Original languageEnglish
Title of host publicationUNCECOMP 2017 - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
EditorsGeorge Stefanou, M. Papadrakakis, Vissarion Papadopoulos
Place of PublicationAthens
Pages90-99
Number of pages10
ISBN (Electronic)9786188284449
DOIs
Publication statusPublished - 17 Jun 2017
Event2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2017 - Rhodes Island, Greece
Duration: 15 Jun 201717 Jun 2017

Conference

Conference2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2017
CountryGreece
CityRhodes Island
Period15/06/1717/06/17

Keywords

  • binary decision diagrams
  • Imprecision
  • lifeline networks
  • reliability analysis
  • survival signature
  • uncertainty assessment

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  • Cite this

    Feng, G., Reed, S., Patelli, E., Beer, M., & Coolen, F. P. A. (2017). Efficient reliability and uncertainty assessment on lifeline networks using the survival signature. In G. Stefanou, M. Papadrakakis, & V. Papadopoulos (Eds.), UNCECOMP 2017 - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (pp. 90-99). https://doi.org/10.7712/120217.5354.16865