Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Development of machine learning model for reefer container failure determination and cause analysis with unbalanced data

불균형 데이터를 갖는 냉동 컨테이너 고장 판별 및 원인 분석을 위한 기계학습 모형 개발

  • 이희원 (동아대학교 경영정보학과) ;
  • 박성호 (동아대학교 경영정보학과) ;
  • 이승현 (동아대학교 경영정보학과) ;
  • 이승재 (동아대학교 경영정보학과) ;
  • 이강배 (동아대학교 경영정보학과)
  • Received : 2021.10.16
  • Accepted : 2022.01.20
  • Published : 2022.01.28
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Abstract

The failure of the reefer container causes a great loss of cost, but the current reefer container alarm system is inefficient. Existing studies using simulation data of refrigeration systems exist, but studies using actual operation data of refrigeration containers are lacking. Therefore, this study classified the causes of failure using actual refrigerated container operation data. Data imbalance occurred in the actual data, and the data imbalance problem was solved by comparing the logistic regression analysis with ENN-SMOTE and class weight with the 2-stage algorithm developed in this study. The 2-stage algorithm uses XGboost, LGBoost, and DNN to classify faults and normalities in the first step, and to classify the causes of faults in the second step. The model using LGBoost in the 2-stage algorithm was the best with 99.16% accuracy. This study proposes a final model using a two-stage algorithm to solve data imbalance, which is thought to be applicable to other industries.

냉동 컨테이너의 고장은 큰 비용의 손실을 야기하지만, 현재 냉동 컨테이너의 알람 체계는 효율성이 떨어진다. 기존에 냉동 시스템의 시뮬레이션 데이터를 활용한 연구는 존재하지만, 냉동 컨테이너의 실제 운영 데이터를 활용한 연구는 부족하다. 이에 본 연구는 실제 냉동 컨테이너 운영 데이터를 활용하여 고장 원인을 분류하였다. 실제 데이터에서는 데이터 불균형이 발생하였으며 ENN-SMOTE, 클래스 가중치를 둔 Logistic 회귀분석과 본 연구에서 개발한 2-stage 알고리즘을 비교하여 데이터 불균형문제를 해결하였다. 2-stage 알고리즘은 XGboost, LGBoost, DNN을 사용하여 첫 번째 단계에서는 고장 및 정상을 분류하고, 두 번째 단계에서는 고장의 원인을 분류하는 알고리즘이다. 2-stage 알고리즘에서 LGBoost를 사용한 모델이 99.16%의 정확도로 가장 우수하였다. 본 연구는 데이터 불균형을 해결하기 위해 2-stage 알고리즘을 활용한 최종모델을 제안하며 이는 다른 산업에도 활용할 수 있을 것으로 사료된다.

Keywords

Acknowledgement

This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-02091,Development and Commercialization of IoT-based refrigerated container real-time monitoring and BigData / AI-based failure predictive service platform to strengthen competitiveness of shipping & logistics company).

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