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An introduction to statistical flowgraph models for engineering systems

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  • David H Collins
  • Richard L Warr
  • Aparna V Huzurbazar

Abstract

Statistical flowgraph models have proven useful for analysis and modeling of complex systems viewed as multistate processes that lead to outcomes such as degraded operation or failure. This article provides an engineering-oriented introduction to statistical flowgraph models: system representation, setting up a flowgraph model, parameter estimation, solution of the model (using either a frequentist or Bayesian approach), and interpretation of model outputs. The method is illustrated with a model for piping reliability in a nuclear power plant, and compared with alternative solution methods.

Suggested Citation

  • David H Collins & Richard L Warr & Aparna V Huzurbazar, 2013. "An introduction to statistical flowgraph models for engineering systems," Journal of Risk and Reliability, , vol. 227(5), pages 461-470, October.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:5:p:461-470
    DOI: 10.1177/1748006X13481927
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    References listed on IDEAS

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    1. Brian J. Williams & Aparna V. Huzurbazar, 2006. "Posterior sampling with constructed likelihood functions: an application to flowgraph models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(2), pages 127-137, March.
    2. Joseph Abate & Ward Whitt, 1995. "Numerical Inversion of Laplace Transforms of Probability Distributions," INFORMS Journal on Computing, INFORMS, vol. 7(1), pages 36-43, February.
    3. David H. Collins & Aparna V. Huzurbazar, 2012. "Prognostic models based on statistical flowgraphs," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(2), pages 141-151, March.
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