Proportional hazard modeling for hierarchical systems with multi-level information aggregation

Mingyang Li, Qingpei Hu, Jian Liu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Reliability modeling of hierarchical systems is crucial for their health management in many mission-critical industries. Conventional statistical modeling methodologies are constrained by the limited availability of reliability test data, especially when the system-level reliability tests of such systems are expensive and/or time-consuming. This article presents a semi-parametric approach to modeling system-level reliability by systematically and explicitly aggregating lower-level information of system elements; i.e., components and/or subsystems. An innovative Bayesian inference framework is proposed to implement information aggregation based on the known multi-level structure of hierarchical systems and interaction relationships among their composing elements. Numerical case study results demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)149-163
Number of pages15
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number2
StatePublished - 2014


  • Bayesian inference
  • Information aggregation
  • Multi-level structure
  • Prior elicitation
  • System reliability

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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