Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
System reliability estimation and confidence regions from subsystem and full system tests
ACC'09 Proceedings of the 2009 conference on American Control Conference
Parameter estimation problems with singular information matrices
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
This paper provides an overview and examples of a novel and practical method for estimating the reliability of a complex system, with confidence regions, based on a combination of full system and subsystem (and/or component or other) tests. It is assumed that the system is composed of multiple processes, where the subsystems may be arranged in series, parallel, combination series/parallel, or other mode. Maximum likelihood estimation (MLE) is used to estimate the overall system reliability based on the fusion of system and subsystem test data. The method is illustrated on two real-world systems: an aircraft-missile system and a highly reliable low-pressure coolant injection system in a commercial nuclear-power reactor. The examples are used to demonstrate the following properties of the method. One, increasing the number of full system tests improves the confidence in the full system reliability estimate. Two, increasing the number of tests of one subsystem stabilizes the subsystem reliability estimate. Three, the likelihood function and optimization constraints can readily be modified to handle systems consisting of repeated components in a mixed series/parallel configuration. Four, the asymptotic normal assumption for computing confidence intervals is not always appropriate, especially for highly reliable systems. Five, performing a mixture of full system and subsystem tests is important when the model that relates the subsystem reliability to the full system reliability is uncertain.