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Comparisons of Different Importance Measures with Hierarchical System Markov Bayesian Network Model

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Importance measures are widely used in reliability analysis to identify weak components in the whole system and support maintenance decisions. In this paper, three different importance measures, namely Griffith importance measure (GIM), Wu importance measure (Wu IM) and integrated importance measure (IIM), are utilized to analyze weak components in hierarchical systems based on the hierarchical system Markov Bayesian network (HSMBN). First, HSMBN is conducted to describe the hierarchical system considering the dependent relationship among components and subsystems. The discrete-state Markov process model is then conducted to describe component or system degradation processes. A reliability information aggregation method is proposed based on the HSMBN under a Bayesian framework. The three importance measures mentioned above are then introduced and incorporated into the HSMBN. Finally, a case study on an aircraft electro-hydrostatic actuator (EHA) is used to verify the proposed model. The results show that HSMBN provides a comprehensive system model to analyze the dependent relationships among components and that different importance measures based on HSMBN are effective for identifying key states affecting the system performance.

Original languageEnglish
Title of host publication2021 Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021
EditorsWei Guo, Steven Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665401302
DOIs
StatePublished - 2021
Event12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021 - Nanjing, China
Duration: 15 Oct 202117 Oct 2021

Publication series

Name2021 Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021

Conference

Conference12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021
Country/TerritoryChina
CityNanjing
Period15/10/2117/10/21

Keywords

  • Bayesian network
  • Hierarchical system
  • Importance measure
  • Information aggregation

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