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Optimum combination of full system and subsystem tests for estimating the reliability of a system

Published: 21 September 2009 Publication History

Abstract

This paper develops a method for finding an optimum test plan, which consists of a mixture of full system and subsystem tests, to estimate the reliability of a system. An optimum test plan is developed by trading off the number of full system and subsystem tests to minimize the meansquared error (MSE) of the maximum likelihood estimate (MLE) of system reliability and testing costs. The MSE is decomposed into the variance of the MLE and a bias from incorrectly specifying the function that relates the subsystem reliabilities to the full system reliability (series, parallel, other). The variance of the MLE comes from Fisher theory. The bias is due to the modeling error. Optimum test plans involve trade offs between the MSE (estimation accuracy), the degree of modeling error, and the cost of doing system and subsystem tests. A Pareto frontier can be identified, as illustrated in the paper.

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  1. Optimum combination of full system and subsystem tests for estimating the reliability of a system

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      PerMIS '09: Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
      September 2009
      322 pages
      ISBN:9781605587479
      DOI:10.1145/1865909
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 21 September 2009

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      Author Tags

      1. maximum likelihood estimation
      2. mean-squared error
      3. model bias
      4. reliability
      5. test sizing

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      PerMIS '09
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      PerMIS '09: Performance Metrics for Intelligent Systems
      September 21 - 23, 2009
      Maryland, Gaithersburg

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