Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
