Case study on quality prediction of the ammunition stockpile reliability program based on a small amount of discontinuous data

Authors

  • Namsu Ahn Korea Military Academy
  • Jaewoong Kim Korea Military Academy
  • Minsu Kim Korea Military Academy
  • Jonggill Lee Korea Military Academy

DOI:

https://doi.org/10.37944/jams.v2i1.39

Keywords:

Ammunition Stockpile Reliability Program, Markov Analysis, Bayes' Theorem, Lifetime Estimation

Abstract

This research proposes a new framework on reliability assessments of stored ammunition stocks. Many previous studies on reliability assessments are based on the correlation between the content of residual stabilizer and the year of manufacturing. However, it ignores the quality difference of the lot and other possible deterioration factors. In this research, we suggest a new framework which can overcome those shortcomings. To estimate the life of the stored ammunition, this research combined two popular techniques. The first one is Markov analysis and the second one is Bayes’ theorem. Markov analysis is used to represent the discontinuous experimental interval and quality difference in the lot, and Bayes’ theorem is used to overcome the circumstance that information from the experiments is limited. We obtained data from previous research article, and calculated three state transition matrix(function, non-function, stability) to apply the Markov analysis. Weights from the opinions of experts are given to three matrix, and the calculated total state transition matrix is used to estimate the long term ammunition status. In this paper, we propose a new framework which can reflect the quality degradation in lot and state-based business procedure. Also, the business reality that number of possible experiments is small and the observation period can be discontinuous are reflected.

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Published

2019-05-03 — Updated on 2019-05-10

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How to Cite

Ahn, N., Kim, J., Kim, M., & Lee, J. (2019). Case study on quality prediction of the ammunition stockpile reliability program based on a small amount of discontinuous data. Journal of Advances in Military Studies, 2(1), 1-14. https://doi.org/10.37944/jams.v2i1.39 (Original work published May 3, 2019)