DOI QR코드

DOI QR Code

Identify the Failure Mode of Weapon System (or equipment) using Machine Learning

Machine Learning을 이용한 무기 체계(or 구성품) 고장 유형 식별

  • Received : 2018.05.23
  • Accepted : 2018.08.03
  • Published : 2018.08.31

Abstract

The development of weapon systems (or components) is hindered by the number of tests due to the limited development period and cost, which reduces the scale of accumulated data related to failures. Nevertheless, because a large amount of failure data and maintenance details during the operational period are managed by computerized data, the cause of failure of weapon systems (or components) can be analyzed using the data. On the other hand, analyzing the failure and maintenance details of various weapon systems is difficult because of the variation among groups and companies, and details of the cause of failure are described as unstructured text data. Fortunately, the recent developments of big data processing technology, machine learning algorithm, and improved HW computation ability have supported major research into various methods for processing the above unstructured data. In this paper, unstructured data related to the failure / maintenance of defense weapon systems (or components) is presented by applying doc2vec, a machine learning technique, to analyze the failure cases.

Keywords

Machine learning;doc2vec;Clustering;Visualization;Failure mode;Weapon System

References

  1. Sunghoon Cho, Sukho Kang, "Machine Learning(AI) industrial application", Industrial Engineering Magazine 23(2), pp.34-38, 2016.06
  2. Establishment of follow-up support system for export of weapons system, Konkuk Univ. Industry-Academia Collaboration Foundation Military Vision Lab, pp. 114-115,2015.12
  3. Sooyune Jeon, Donghun Lee, Manjae Bae, "Study on the Application Method of Munition's Quality Information based on Big Data", Journal of the Korea Academia-Industrial cooperation Society, Vol 17, pp.315-325, 2016
  4. Hyun-jung Kim, "Big Data Concept and Big Data Analysis Technique", Seminar, Korean Transport Institute.
  5. Estabilishment of follow-up support system for export of weapons system, Konkuk Univ. Industry-Academia Collaboration Foundation Military Vision Lab, p. summary-3, 2015.12
  6. Jongmoon Rhee, Jongshin Lee, Seungryool Lee , Kyungduk Park, "A Study on FMECA based on failure rate and cost of occurrence", Korean Institute Of Industrial Engineers, pp.841-845, 2010.11
  7. Andreas Muller, Sarah Guido(2017), Introduction to Machine Learning with Python, O'REILLY,
  8. Gavagai, A BRIEF HISTORY OF WORD EMBEDDINGS (AND SOME CLARIFICATIONS), Gavagai, 2015.9.30., Available From: https://www.gavagai.se/blog/2015/09/30/a-brief-history-of-word-embeddings (accessed Mar., 30, 2018)
  9. Mikolov Tomas, Sutskerver Ilya, Chen Kai, Corrado Greg, Dean Jeffrey. "Distributed Representations of Words and Phrases and their Compositionality", In Advances on Neural Information Processing System, pp.3111-3119, 2013
  10. Lau, Baldwin, An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation, arXiv.org, 2016.7.19.