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Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions.

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Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice.

Leonardo Leoni1, Farshad BahooToroody2, Saeed Khalaj2

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Bayesian inference enhances reliability analysis by incorporating prior knowledge, outperforming traditional methods. Strong informative priors significantly impact failure predictions, guiding maintenance and asset management.

Keywords:
beta-binomial failure modellinghierarchical Bayesian modellingprior informationreliability analysis

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Area of Science:

  • Engineering
  • Statistics
  • Risk Management

Background:

  • Reliability analysis is crucial for risk and asset integrity management.
  • Bayesian inference offers advantages over Maximum Likelihood Estimation (MLE) and Least Square Estimation (LSE) for failure modeling parameter estimation.
  • Bayesian methods can integrate prior information, mitigating data scarcity issues.

Purpose of the Study:

  • To present a mathematical framework for Bayesian reliability assessment of industrial components.
  • To investigate how the choice of prior distribution influences predictions of future failures.
  • To demonstrate the application of hierarchical Bayesian modeling (HBM) and beta-binomial distribution for failure behavior.

Main Methods:

  • Hierarchical Bayesian modeling (HBM) applied to three datasets of varying sizes.
  • Consideration of five distinct prior distributions.
  • Utilizing a beta-binomial distribution to model device failure behavior.

Main Results:

  • The selection of strong informative priors demonstrably alters failure predictions.
  • The impact of prior choice on predictions persists even with larger sample sizes.
  • Hierarchical Bayesian modeling provides a robust framework for reliability assessment.

Conclusions:

  • Prior beliefs significantly influence reliability predictions in industrial components.
  • Maintenance engineers and asset managers can leverage prior knowledge within Bayesian frameworks.
  • This research provides a valuable tool for enhancing asset integrity and risk management strategies.