Reliability modeling and analysis of safety-critical manufacture system

Qing Sun, Lirong Cui, Gong Chen, Rong Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

There are working, fail-safe and fail-dangerous states in safety-critical manufacture systems. This paper presents three typical safety-critical manufacture system architecture models: series, parallel and series-parallel system, whose components lifetime distributions are general forms. Also the reliability related indices, such as the probabilities that the system in these states and the mean times for the system fail-safe and fail-dangerous, are derived respectively. And the relationships among the obtained indices of the three system architecture models are analyzed. Finally some numerical examples are employed to elaborate the results obtained in this paper. The derived indices formulas are new results and without component lifetime distribution assumptions, which have significant meaning for evaluating the manufacture system reliability and improving the manufacture system safety design.

Original languageEnglish (US)
Title of host publication2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009
Pages560-563
Number of pages4
DOIs
StatePublished - Dec 18 2009
Event2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009 - Changsha, Hunan, China
Duration: Oct 10 2009Oct 11 2009

Publication series

Name2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009
Volume2

Other

Other2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009
Country/TerritoryChina
CityChangsha, Hunan
Period10/10/0910/11/09

Keywords

  • Fail-dangerous
  • Fail-safe
  • Mean time to failure
  • Reliabilty analysis
  • Safety system

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Control and Systems Engineering

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