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Optimal discrete stopping times for reliability growth tests

Quigley, J.L. (2005) Optimal discrete stopping times for reliability growth tests. International Journal of Reliability, Qualily and Safety Engineering, 12 (5). pp. 365-383. ISSN 0218-5393

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    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.

    Item type: Article
    ID code: 4360
    Notes: Electronic version of an article published as Int. J. Rel. Qual. Saf. Eng., 12, 365, (2005) © Copyright World Scientific Publishing Company.
    Keywords: reliability engineering, management thory, reliability growth models, statistics, Management. Industrial Management, Risk Management, Statistics, Energy Engineering and Power Technology, Nuclear Energy and Engineering, Industrial and Manufacturing Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality, Computer Science(all), Aerospace Engineering
    Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
    Social Sciences > Industries. Land use. Labor > Risk Management
    Social Sciences > Statistics
    Department: Strathclyde Business School > Management Science
    Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 22 Oct 2007
    Last modified: 10 Jul 2014 01:07

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