DOI QR코드

DOI QR Code

Study on Text Analysis of the Liquefied Natural Gas Carriers Dock Specification for Development of the Ship Predictive Maintenance Model

선박예지정비모델 개발을 위한 LNG 선박 도크 수리 항목의 텍스트 분석 연구

  • Hwang, Taemin (Department of Maritime Transportation System Mokpo National Maritime University) ;
  • Youn, Ik-Hyun (Division of Navigation & Information Systems, Mokpo National Maritime University) ;
  • Oh, Jungmo (Division of Marine Engineering, Mokpo National Maritime University)
  • 황태민 (목포해양대학교 해상운송시스템학부) ;
  • 윤익현 (목포해양대학교 항해정보시스템학부) ;
  • 오정모 (목포해양대학교 기관시스템공학부)
  • Received : 2020.12.01
  • Accepted : 2020.12.24
  • Published : 2021.02.28

Abstract

The importance of maintenance is leading the application of the maintenance strategy in various industries. The maritime industry is also a part of them, with changes in selecting and applying the maintenance strategy, but rather slowly, by retaining the old strategy. In particular, the ship is maintaining a previously used strategy. In the circumstance of the sea, ship requires a new suggestion for maintenance strategy. A ship predictive maintenance model predicts the breakdown or malfunction of machineries to secure maintenance time with preventive actions and treatments, thereby avoiding maintenance-related dangerous factors. This study focused on applying text analysis to an Liquefied Natural Gas Carriers dock indent document, and the analysis results were interpreted from the original document. The inter-relational patterns observed from the frequency of common maintenance combinations among different parts and equipment in ships will be applied to the development of ship predictive maintenance.

다양한 산업에서 강조되고 있는 정비의 중요성은 각 분야에 다양한 정비전략을 적용하도록 만들었다. 해양산업 역시 그에 따른 정비전략의 변화가 있었으나 타 산업 대비 그 속도가 느려 실제 적용이 되지 않은 채 과거 시행되고 있던 방식을 유지하는 경우가 많다. 특히 선박은 기존에 행해왔던 방식의 정비전략을 사용하고 있는 편이며 해상의 조건에서 선박은 새로운 정비전략의 개발을 필요로 하고있다. 이에 선박예지정비모델은 기기의 정비가 필요한 시점을 예지하여 조치 할 수 있는 정비전략으로서 선박이 항해 중에 처할 수 있는 정비 관련 위험요소들을 줄여 주는 모델이다. 본 연구는 선박예지정비모델의 개발을 위한 연구 중의 하나로서, LNG선박 입거사양서의 텍스트 데이터 분석을 통한 결과를 원문의 내용을 바탕으로 해석해보았다. 공통된 정비항목 조합을 도출하여 선박 내 다른 기기들 사이에 작용하고 있는 상호연관성을 발견하고 이를 앞으로 개발될 선박예지정비모델에 적용하고자 한다.

Keywords

References

  1. Emovon, I., R. A. Norman, and A. J. Murphy(2018), Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems, Journal of intelligent manufacturing, 29(3), pp. 519-531. https://doi.org/10.1007/s10845-015-1133-6
  2. Gesmundo, A. and T. Samardzic(2012), Lemmatisation as a tagging task, In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Vol. 2: Short Papers), pp. 368-372.
  3. Gkerekos, C., I. Lazakis, and G. Theotokatos(2017), Ship machinery condition monitoring using performance data through supervised learning, Proceedings of the 2017 Smart Ship Technology Conference, ISBN 9781909024632, pp. 105-111.
  4. Goldsworthy, L. and I. E. Galbally(2011), Ship engine exhaust emissions in waters around Australia-an overview, Air Quality and Climate Change, 45(4), p. 24.
  5. Jelodar, H., Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li, and L. Zhao(2019), Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey, Multimedia Tools and Applications, 78(11), pp. 15169-15211. https://doi.org/10.1007/s11042-018-6894-4
  6. Kandemir, C. and M. Celik(2019), A human reliability assessment of marine auxiliary machinery maintenance operations under ship PMS and maintenance 4.0 concepts, Cognition, Technology & Work, 22(3), pp. 473-487. https://doi.org/10.1007/s10111-019-00590-3
  7. KR(2019), Korea Register, 2019 KR-Rules & Guidance, www.krs.co.kr (Accessed 11 January 2021).
  8. Sethy, A. and B. Ramabhadran(2008), Bag-of-word normalized n-gram models, INTERSPEECH 2008, 9th Annual Conference of the International Speech Communication Association, Brisbane, Australia, September 22-26, 2008.
  9. Vijayarani, S. and R. Janani(2016), Text mining: open source tokenization tools-an analysis, Advanced Computational Intelligence: An International Journal (ACII), 3(1), pp. 37-47. https://doi.org/10.5121/acii.2016.3104