RTG Student Work Group

Comparing Competing Resource Allocation Strategies Using Entropy
and "Strategic Binning" for Reliability of Complex Systems

Jessica Chapman
Abstract • September 17, 2007

A primary goal in complex system reliability is to estimate the reliability of the full system. The most direct method of obtaining this estimate would be to perform many full system tests. However, in practice, the number of these tests that are actually performed can be rather small because they are either too expensive or impossible to perform. In many cases, however, it may be possible to obtain data from other less direct, but still informative, sources to aid in the estimation of the full system reliability. Quality assurance data, maintenance data or measurements from common components in related systems are all examples of these other sources of data. Our interest in the problem involves resource allocation. Data from the different sources have different inherent value and collection costs. It is important, and challenging, to allocate additional resources among these different data sources so as to obtain the most information about the reliability of the full system.

This talk will focus on the resource allocation problem in the context of a k-component series system which has pass/fail data, not dependent upon time, available at component and system levels. Entropy is commonly used in Bayesian experimental design and is proposed as a new metric for quantifying information gain in this resource allocation problem. The current approach to evaluating a given metric is computationally intensive and involves repeated runs of an MCMC program. A potentially time-saving approach requiring only a single MCMC run will be presented.