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

Mingyang Li, Qingpei Hu, Jian Liu

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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)
Volume46
Issue number2
DOIs
StatePublished - 2014

Fingerprint

Hierarchical systems
Hazards
Agglomeration
Health
Availability
Industry

Keywords

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

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Proportional hazard modeling for hierarchical systems with multi-level information aggregation. / Li, Mingyang; Hu, Qingpei; Liu, Jian.

In: IIE Transactions (Institute of Industrial Engineers), Vol. 46, No. 2, 2014, p. 149-163.

Research output: Contribution to journalArticle

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