Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
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Analysis of Ammunition Inspection Record Data and Development of Ammunition Condition Code Classification Model

탄약검사기록 데이터 분석 및 탄약상태기호 분류 모델 개발

  • Young-Jin Jung (Department of Industrial Engineering, INHA University) ;
  • Ji-Soo Hong (Department of Industrial Engineering, INHA University) ;
  • Sol-Ip Kim (PGM R&D Institute, Hanwha Aerospace) ;
  • Sung-Woo Kang (Department of Industrial Engineering, INHA University)
  • 정영진 (인하대학교 산업경영공학과) ;
  • 홍지수 (인하대학교 산업경영공학과) ;
  • 김솔잎 (한화에어로스페이스 PGM 연구소) ;
  • 강성우 (인하대학교 산업경영공학과)
  • Received : 2024.05.01
  • Accepted : 2024.06.04
  • Published : 2024.06.30
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Abstract

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

Keywords

Acknowledgement

이 연구는 2022년도 정부(방위사업청)의 재원으로 국방기술진흥연구소의 지원을 받아 수행된 연구임(KRIT-CT-22-081, 무기체계 CBM+ 특화연구센터).

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