Journal of Applied Reliability (한국신뢰성학회지:신뢰성응용연구)

The Korean Reliability Society (한국신뢰성학회)
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Optimal Maintenance Policy Using Non-Informative Prior Distribution and Marcov Chain Monte Carlo Method

사전확률분포와 Marcov Chain Monte Carlo법을 이용한 최적보전정책 연구

  • Received : 2017.06.09
  • Accepted : 2017.09.12
  • Published : 2017.09.25
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Abstract

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.

Keywords

References

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