Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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Reduced Order Modeling of Marine Engine Status by Principal Component Analysis

주성분 분석을 통한 선박 기관 상태의 차수 축소 모델링

  • Seungbeom Lee (Department of Naval Architecture and Ocean Engineering, Chungnam National University) ;
  • Jeonghwa Seo (Department of Naval Architecture and Ocean Engineering, Chungnam National University) ;
  • Dong-Hwan Kim (Institute of Advanced Transportation Vehicles, Chungnam National University) ;
  • Sangmin Han (Autonomous Ship Research Center, Samsung Heavy Industries Co., LTD.) ;
  • Kwanwoo Kim (Autonomous Ship Research Center, Samsung Heavy Industries Co., LTD.) ;
  • Sungwook Chung (Autonomous Ship Research Center, Samsung Heavy Industries Co., LTD.) ;
  • Byeongwoo Yoo (Department of Autonomous Vehicle System Engineering, Chungnam National University)
  • 이승범 (충남대학교 선박해양공학과) ;
  • 서정화 (충남대학교 선박해양공학과) ;
  • 김동환 (충남대학교 첨단수송체연구소) ;
  • 한상민 ((주)삼성중공업 조선해양연구소 자율운항연구센터) ;
  • 김관우 ((주)삼성중공업 조선해양연구소 자율운항연구센터) ;
  • 정성욱 ((주)삼성중공업 조선해양연구소 자율운항연구센터) ;
  • 유병우 (충남대학교 자율운항시스템공학과)
  • Received : 2023.10.04
  • Accepted : 2023.12.08
  • Published : 2024.02.20
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Abstract

The present study concerns reduced order modeling of a marine diesel engine, which can be used for outlier detection in status monitoring and carbon intensity index calculation. Principal Component Analysis (PCA) is introduced for the reduced order modeling, focusing on the feasibility of detecting and treating nonlinear variables. By cross-correlation, it is found that there are seven non-linear data channels among 23 data channels, i.e., fuel mode, exhaust gas temperature after the turbocharger, and cylinder coolant temperatures. The dataset is handled so that the mean is located at the nominal continuous rating. Polynomial presentation of the dataset is also applied to reflect the linearity between the engine speed and other channels. The first principal mode shows strong effects of linearity of the most data channels to show the linearity of the system. The non-linear variables are effectively explained by other modes. second mode concerns the temperature of the cylinder cooling water, which shows small correlation with other variables. The third and fourth modes correlates the fuel mode and turbocharger exhaust gas temperature, which have inferior linearity to other channels. PCA is proven to be applicable to data given in binary type of fuel mode selection, as well as numerical type data.

Keywords

Acknowledgement

본 연구는 2023년 삼성중공업의 재원으로 충남대학교에서 수행된 '자율운항시스템 핵심 기술 개발'과제 및 22023년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원을 받아 수행된 연구임(P0017006, 2023년 산업혁신인재성장지원사업).

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