Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
권호 표지
DOI QR코드

DOI QR Code

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

Naive Bayes-LSTM 기반 예지정비 플랫폼 적용을 통한 화물 상차 시스템의 운영 안전성 및 신뢰성 확보 연구

  • 황선우 (아주대학교 시스템공학과) ;
  • 김진오 ((주)코어디아이티) ;
  • 최준우 ((주)노바 기업부설연구소) ;
  • 김영민 (아주대학교 시스템공학과)
  • Received : 2023.11.20
  • Accepted : 2023.12.14
  • Published : 2023.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

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.

Keywords

Acknowledgement

본 논문은 산업통상자원부의 지원을 받아 수행되었음.(20015047)

References

  1. T. M. Hwang, I. H. Youn, J. M. Oh(2021), "Study on text analysis of the liquefied natural gas carriers dock specification for development of the ship predictive maintenance model." Journal of the Korean Society of Marine Environment & Safety, 27(1):60-66.  https://doi.org/10.7837/kosomes.2021.27.1.060
  2. I. H. Youn, J. K. Park, J. M. Oh(2021), "A study on the concept of a ship predictive maintenance model reflection ship operation characteristics." Journal of the Korean Society of Marine Environment & Safety, 27(1):53-59.  https://doi.org/10.7837/kosomes.2021.27.1.053
  3. C. W. Hong(2022), "A study on the application of predictive maintenance using artificial intelligence and big data." The Quarterly Journal of Defense Policy Studies, 38(2):197-228.  https://doi.org/10.22883/JDPS.2022.38.2.006
  4. K. M. Cheon, J. K. Yang(2021), "Explainable AI application for machine predictive maintenance." Journal of Korean Society of Industrial and Systems Engineering, 44(4):227-233.  https://doi.org/10.11627/jksie.2021.44.4.227
  5. K. H. Lee, J. Y. Lee, Y. M. Kim(2022), "A study on the maintenance data analysis of vehicle parts of Yongin light rail and condition-based prediction maintenance." Journal of the Korea Society of Systems Engineering, 18(1):1-13.  https://doi.org/10.14248/JKOSSE.2022.18.1.001
  6. J. D. Lee, G. B. Kim, I. H. Song(2021), "A study on the predictive maintenance of smart factory through ML-Based analysis of vibration abnormal signal." Journal of The Korea Society of Information Technology Policy & Management(ITPM), 13(6):2723-2728. 
  7. C. S. Park, S. M. Bae(2020), "A study on the predictive maintenance of 5 Axis CNC machine tools for cutting of large aircraft parts." Journal of Korean Society of Industrial and Systems Engineering, 43(4):161-167.  https://doi.org/10.11627/jkise.2020.43.4.161
  8. J. D. Lee, H. S. Kim, J. H. Kim(2019), "A study on AI-based anomaly detection and defect classification for predictive maintenance of Gearb." Journal of The Korea Society of Information Technology Policy & Management(ITPM), 11(6):1497-1502. 
  9. J. T. Kim, Y. W. Seo, S. S. Lee, S. J. Kim, Y. G. Kim(2021), "A proposal of remaining useful life prediction model for turbofan engine based on k-nearest neighbor." Journal of Korea Academia-Industrial Cooperation Society(JKAIS), 22(4):611-620.  https://doi.org/10.5762/KAIS.2021.22.10.447
  10. J. D. Kim, H. J. Lee(2019), "A study on predictive model for forecasting anti-aircraft missile spare parts demand based on machine learning." Journal of the Korean Data And Information Science Society, 30(3):587-596.  https://doi.org/10.7465/jkdi.2019.30.3.587
  11. C. Nguyen, T. M. Hoang, A. A. Cheema(2023), "Channel rstimation using CNN-LSTM in RIS-NOMA assisted 6G network." IEEE Transactions on Machine Learning in Communications and Networking, 1:43-60.  https://doi.org/10.1109/TMLCN.2023.3278232
  12. X. Gao, J. Wang, M. Zhou(2023), "The Research of resource allocation method based on GCN-LSTM in 5G network." IEEE Communications Letters, 27(3):926-930.  https://doi.org/10.1109/LCOMM.2022.3224213
  13. X. Wen, W. Li(2023), "Time series prediction based on LSTM-attention-LSTM model." IEEE Access, 11:48322-48331.  https://doi.org/10.1109/ACCESS.2023.3276628
  14. S. W. Hwang, Y. M. Kim(2023), "On the establishment of LSTM-based predictive maintenance platform to secure the operational reliability of ICT/cold-chain unmanned storage." International Journal of Advanced Smart Convergence, 12(3):207-218. 
  15. S. Hochreiter, J. Schmidhuber(1997), "Long short-term memory." Neural Computation, 9:1735-1780.  https://doi.org/10.1162/neco.1997.9.8.1735
  16. B. Thomas, R. Price(1763), Philosophical transactions of the royal society of London. 53:370-418.  https://doi.org/10.1098/rstl.1763.0053
  17. H. R. Jeong, H. H. Kim, S. M. Park, E. Han, K. H. Kim, I. S. Yun(2017), "Prediction of severities of rental car traffic accidents using naive bayes big data classifier." The Journal of the Korea Institute of Intelligent Transportation Systems, 16(4):1-12.  https://doi.org/10.12815/kits.2017.16.4.01
  18. S. K. Kang, B. K. Kwon, C. W. Kwon, S. M. Park, I. S. Yun(2018), "Development of incident detection algorithm using naive bayes classification." The Journal of The Korea Institute of Intelligent Transportation Systems, 17(6):25-39.  https://doi.org/10.12815/kits.2018.17.6.25
  19. H. J. Kim, J. Y. Jang(2008), "Improving naive bayes text classifiers with incremental feature weighting." KIPS Transactions on Software and Data Engi neering, 15(5):457-464.  https://doi.org/10.3745/KIPSTB.2008.15-B.5.457
  20. J. I. Kim, S. J. Park, H. G. Kim, J. H. Choi, H. I. Kim, P. K. Kim(2020), "Sensitivity identification method for new words of social media based on naive bayes classification."Smart Media Journal, 9(1):51-59. https://doi.org/10.30693/SMJ.2020.9.1.51