Resource allocatiion: sequential data collection for reliability analysis involving systems and component level data

Conference · Fri Jan 01 04:00:00 EST 2010

OSTI ID:993116

Anderson-cooke, Christine M ^[1]

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1. Los Alamos National Laboratory

In analyzing the reliability of complex systems, several types of data from full-system tests to component level tests are commonly available and are used. After a preliminary analysis, additional resources may be available to collect new data. The goal of resource allocation is to identify the best new data to collect to maximally improve the prediction of system reliability. While several possible definitions of 'maximally improve' are possible, we focus on reducing the uncertainty or the width of the uncertainty interval for the prediction of system reliability at a user-specified age(s). In this paper, we present an algorithm that allows us to estimate the anticipated improvement to the analysis with the addition of new data, based on current understanding of all of the statistical model parameters. This quantitative assessment of the anticipated improvement can be helpful to justify the benefits of collecting new data. Additionally by comparing different potential allocations, it is possible to determine what new data should be collected to improve our understanding of the response. This optimization takes into account the relative cost of different data types and can be based on flexible allocation options, or subject to logistical constraints.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: DOE
• DOE Contract Number: AC52-06NA25396
• OSTI ID: 993116
• Report Number(s): LA-UR-10-01391; LA-UR-10-1391
• Country of Publication: United States
• Language: English

Related Subjects

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE
97 MATHEMATICS AND COMPUTING
ALGORITHMS
FORECASTING
OPTIMIZATION
RELIABILITY
STATISTICAL MODELS