Optimal discrete stopping times for reliability growth tests

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided.
LanguageEnglish
Pages365-383
Number of pages18
JournalInternational Journal of Reliability, Qualily and Safety Engineering
Volume12
Issue number5
DOIs
Publication statusPublished - 2005

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Fault detection
Testing
Random processes
Maximum likelihood
Statistics

Keywords

  • reliability engineering
  • management thory
  • reliability growth models
  • statistics

Cite this

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title = "Optimal discrete stopping times for reliability growth tests",
abstract = "Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided.",
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Optimal discrete stopping times for reliability growth tests. / Quigley, J.L.

In: International Journal of Reliability, Qualily and Safety Engineering, Vol. 12, No. 5, 2005, p. 365-383.

Research output: Contribution to journalArticle

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