대한안전경영과학회지 (Journal of the Korea Safety Management & Science)

  • 제25권4호
  • /
  • Pages.141-151
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  • 2023
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  • 1229-6783(pISSN)
  • /
  • 2288-1484(eISSN)
대한안전경영과학회 (Korea Safety Management & Science)
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Naive Bayes-LSTM 기반 예지정비 플랫폼 적용을 통한 화물 상차 시스템의 운영 안전성 및 신뢰성 확보 연구

On the Parcel Loading System of Naive Bayes-LSTM Model Based Predictive Maintenance Platform for Operational Safety and Reliability

  • 황선우 (아주대학교 시스템공학과) ;
  • 김진오 ((주)코어디아이티) ;
  • 최준우 ((주)노바 기업부설연구소) ;
  • 김영민 (아주대학교 시스템공학과)
  • 투고 : 2023.11.20
  • 심사 : 2023.12.14
  • 발행 : 2023.12.30
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초록

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.

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과제정보

본 논문은 산업통상자원부의 지원을 받아 수행되었음.(20015047)

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