Parameter estimation in reliability modeling of distributed detection systems

Q. Long*, M. Xie, S. H. Ng

*Corresponding author for this work

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


Most reliability models are associated with their own parameters which are typically estimated from the history data. For the widely used distributed detection system in fault detection, the system reliability depends on the number of normally working detectors and the accuracy of its local detectors. Parameters of the reliability model of distributed detection system are subject to random variation as the detection system may be used in different purposes and environments. Hence, to evaluate the reliability accurately, it is necessary to obtain the system parameters precisely from the test data we have. In this paper, we present a Bayesian approach to estimate the unknown parameters of distributed detection system from the scarce data and quantify the uncertainty on the system reliability by measure of variance. A simulation is conducted as well to calculate the effect on the system reliability from the uncertainty of the parameters. An example is applied to illustrate the parameter estimation by Bayesian approach.

Original languageEnglish (US)
Title of host publicationDCDS'07 - 1st IFAC Workshop on Dependable Control of Discrete Systems
PublisherIFAC Secretariat
Number of pages6
EditionPART 1
ISBN (Print)9783902661395
StatePublished - 2007
Externally publishedYes

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
ISSN (Print)1474-6670


  • Bayesian approach
  • Distributed detection system
  • Parameter estimation
  • Reliability analysis
  • Uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering


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