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Implementation and application of maximum likelihood reliability estimation from subsystem and full system tests

Published: 28 September 2010 Publication History

Abstract

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.

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C. J. Maranzano and J. C. Spall. Robust test design for reliability estimation with modeling error when combining full system and subsystem tests. In Proceedings of the American Control Conference, number ThB17.6, pages 3741--3746, Baltimore, MD, June-July 2010.
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  • (2020)Multilevel Data Integration with Application in Sensor Networks2020 American Control Conference (ACC)10.23919/ACC45564.2020.9148012(5213-5218)Online publication date: Jul-2020

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  1. Implementation and application of maximum likelihood reliability estimation from subsystem and full system tests

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      PerMIS '10: Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
      September 2010
      386 pages
      ISBN:9781450302906
      DOI:10.1145/2377576
      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: 28 September 2010

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      Author Tags

      1. maximum likelihood estimation
      2. performance evaluation
      3. reliability metric
      4. series and non-series systems
      5. test and evaluation

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      PerMIS '10: Performance Metrics for Intelligent Systems
      September 28 - 30, 2010
      Maryland, Baltimore

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      • (2020)Multilevel Data Integration with Application in Sensor Networks2020 American Control Conference (ACC)10.23919/ACC45564.2020.9148012(5213-5218)Online publication date: Jul-2020

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