Distribution modeling for reliability analysis: Impact of multiple dependences and probability model selection

Fan Wang, Heng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


Reliability analysis requires modeling of joint probability distribution of uncertain parameters, which can be a challenge since the random variables representing the parameter uncertainties may be correlated. For convenience, a Gaussian data dependence is commonly assumed for correlated random variables. This paper first investigates the effect of multidimensional non-Gaussian data dependences underlying the multivariate probability distribution on reliability results. Using different bivariate copulas in a vine structure, various data dependences can be modeled. The associated copula parameters are identified from available statistical information by moment matching techniques. After the development of the vine copula model for representing the multivariate probability distribution, the reliability involving correlated random variables is evaluated based on the Rosenblatt transformation. The impact of data dependence is significant because a large deviation in failure probability is observed, which emphasizes the need for accurate dependence characterization. A practical method for dependence modeling based on limited data is thus provided. The result demonstrates that the non-Gaussian data dependences can be real in practice, and the reliability can be biased if the Gaussian dependence is used inappropriately. Moreover, the effect of conditioning order on reliability should not be overlooked except that the vine structure contains only one type of copula.
Original languageEnglish
Pages (from-to)483-499
Number of pages17
JournalApplied Mathematical Modelling
Publication statusPublished - 1 Jul 2018


  • Copula
  • Data dependence
  • Probability distribution
  • Reliability

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics


Dive into the research topics of 'Distribution modeling for reliability analysis: Impact of multiple dependences and probability model selection'. Together they form a unique fingerprint.

Cite this