TY - GEN
T1 - Parameter estimation in reliability modeling of distributed detection systems
AU - Long, Q.
AU - Xie, M.
AU - Ng, S. H.
PY - 2007
Y1 - 2007
N2 - 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. Copyright © 2007 IFAC.
AB - 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. Copyright © 2007 IFAC.
KW - Bayesian approach
KW - Distributed detection system
KW - Parameter estimation
KW - Reliability analysis
KW - Uncertainty
UR - https://www.scopus.com/pages/publications/79960918127
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-79960918127&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783902661395
VL - 1
SP - 19
EP - 24
BT - IFAC Proceedings Volumes (IFAC-PapersOnline)
T2 - 1st IFAC Workshop on Dependable Control of Discrete Systems, DCDS'07
Y2 - 13 June 2007 through 15 June 2007
ER -