An evidence-based likelihood approach for the reliability of a complex system with overlapped failure data

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Authors

  • Lechang Yang

Research Organisations

External Research Organisations

  • University of Science and Technology Beijing
  • City University of Hong Kong
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Details

Original languageEnglish
Article number110893
Number of pages8
JournalComputers and Industrial Engineering
Volume201
Early online date21 Jan 2025
Publication statusPublished - Mar 2025

Abstract

The reliability evaluation of a complex mechanical system is imperative yet challenging because the experiment of a full-scale system is usually unavailable or prohibitively expensive leading to insufficient or incomplete data. Moreover, the collected data is essentially dependent since it is collected from the same system within the same time period, leading to the so-called “overlapped” failure data. To address the dependence between overlapped data in system reliability analysis, a novel concept called Evidence Likelihood Function (ELF) is developed to decompose the original joint likelihood function. This approach is capable of incorporating dependent evidence in the Bayesian framework and provides us with a better understanding of the nature of dependent evidence in system reliability analysis. It has the potential to optimize the system configuration using less full-scale test data in terms of reliability improvement with lower experiment cost.

Keywords

    Bayesian method, Dependent data, Likelihood function, Probability density function, System reliability

ASJC Scopus subject areas

Cite this

An evidence-based likelihood approach for the reliability of a complex system with overlapped failure data. / Yang, Lechang.
In: Computers and Industrial Engineering, Vol. 201, 110893, 03.2025.

Research output: Contribution to journalArticleResearchpeer review

Download
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