A nonparametric Bayesian network approach to assessing system reliability at early design stages

Dongjin Lee, Rong Pan

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

It is important to predict a system's reliability at its early design stages because modifying design to improve reliability and maintainability at a later time in the system's lifecycle will be costly and, oftentimes, impossible. However, this early prediction is challenging because of the lack of reliability data and the incomplete knowledge of a complex system's reliability structure. To tackle this problem, this paper presents a nonparametric Bayesian network approach. Employing nonparametric Bayesian network, the limitation of discrete Bayesian network can be overcome, and it can be used as a useful tool for decision support. The proposed methodology is applied to a case study to demonstrate its prognostic and diagnostic capabilities.

Original languageEnglish (US)
Pages (from-to)57-66
Number of pages10
JournalReliability Engineering and System Safety
Volume171
DOIs
StatePublished - Mar 2018

Keywords

  • Complex system
  • Copula
  • Graphical models
  • Product design
  • Reliability prediction

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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