Bayesian update and aperiodic maintenance policy for deteriorating systems with unknown parameters

Omshi, E. Mosayebi; Grall, Antoine

doi:10.1201/9781351174664-85

Cited by 2 publications

(1 citation statement)

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“…The method for obtaining the failure probability is the Monte-Carlo simulation [4]. Numerical results from the Monte-Carlo simulation have shown the e ciency of the Bayesian update procedure as a way to adapt the decision rule of the aperiodic maintenance policy [5,6]. In summary, structural reliability is assessed as condition monitoring and operational assessments based on actual and future crack distributions, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian updating of crack distributions in steam generator tubes

Francisco¹,

Simões

2021

VETOR

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The structural failure of steam generator tubes is a common problem that can a ect the availability and safety of nuclear power plants. To minimize the probability of occurrence of failure, it is needed to implement maintenance strategies such as periodic nondestructive inspections of tubes. Thus, a tube is repaired or plugged whenever it has detected a crack which a threshold size is overtaken. In general, uncertainties and errors in crack sizes are associated with the nondestructive inspections. These uncertainties and errors should be appropriately characterized to estimate the actual crack distribution. This work proposes a Bayesian approach for updating crack distributions, which in turn allows computing the failure probability of steam generator tubes at current and future times. The failure criterion is based on plastic collapse phenomenon, and the failure probability is computed by using the Monte-Carlo simulation. The failure probability at current and future times is in good agreement with the ones presented in the literature.

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Section: Introductionmentioning

confidence: 99%