KOREASCHOLAR

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

1. 서 론
2. 예지정비 관련 선행연구 분석
    2.1 선행연구 고찰을 통한 문제 정의
    2.2 연구 절차
3. 화물 상차 시스템의 운영 안정성/신뢰성확보를 위한 예지정비 플랫폼 구축
    3.1 Long Short-Term Memory 모델
    3.2 Naive Bayes 보정 모델
    3.3 화물 상차 시스템 예지정비 플랫폼 구축
4. 화물 상차 시스템의 예지정비 플랫폼구축 및 검증 결과
    4.1 예지정비 플랫폼 웹 구축 결과 평가
    4.2 Naive Bayes-LSTM 연계 예지정비의성능 검증
5. 결 론
6. References
저자 소개

• 김영민(아주대학교 시스템공학과) | Youngmin Kim (Department of Systems Engineering, Ajou University) Corresponding author
• 최준우((주)노바 기업부설연구소) | Junwoo Choi (Research Institute Global Nova)
• 김진오((주)코어디아이티) | Jinoh Kim (CORE DIT)
• 황선우(아주대학교 시스템공학과) | Sunwoo Hwang (Department of Systems Engineering, Ajou University)