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Probability-informed testing for reliability assurance through Bayesian hypothesis methods

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  • Smith, Curtis
  • Kelly, Dana
  • Dezfuli, Homayoon

Abstract

Bayesian inference techniques play a central role in modern risk and reliability evaluations of complex engineering systems. These techniques allow the system performance data and any relevant associated information to be used collectively to calculate the probabilities of various types of hypotheses that are formulated as part of reliability assurance activities. This paper proposes a methodology based on Bayesian hypothesis testing to determine the number of tests that would be required to demonstrate that a system-level reliability target is met with a specified probability level. Recognizing that full-scale testing of a complex system is often not practical, testing schemes are developed at the subsystem level to achieve the overall system reliability target. The approach uses network modeling techniques to transform the topology of the system into logic structures consisting of series and parallel subsystems. The paper addresses the consideration of cost in devising subsystem level test schemes. The developed techniques are demonstrated using several examples. All analyses are carried out using the Bayesian analysis tool WinBUGS, which uses Markov chain Monte Carlo simulation methods to carry out inference over the network.

Suggested Citation

  • Smith, Curtis & Kelly, Dana & Dezfuli, Homayoon, 2010. "Probability-informed testing for reliability assurance through Bayesian hypothesis methods," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 361-368.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:4:p:361-368
    DOI: 10.1016/j.ress.2009.11.006
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    References listed on IDEAS

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    1. Kelly, Dana L. & Smith, Curtis L., 2009. "Bayesian inference in probabilistic risk assessment—The current state of the art," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 628-643.
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    Cited by:

    1. Sonal, S.D. & Ammanagi, S & Kanjilal, O & Manohar, C.S., 2018. "Experimental estimation of time variant system reliability of vibrating structures based on subset simulation with Markov chain splitting," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 55-68.
    2. Betz, Wolfgang & Papaioannou, Iason & Straub, Daniel, 2022. "Bayesian post-processing of Monte Carlo simulation in reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    3. Ahmed, Hussam & Chateauneuf, Alaa, 2014. "Optimal number of tests to achieve and validate product reliability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 242-250.

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