주성분 분석을 통한 선박 기관 상태의 차수 축소 모델링

• Published in: 大韓造船學會 論文集 Vol. 61; no. 1; pp. 8 - 18
• Main Authors: 이승범(Seungbeom Lee), 서정화(Jeonghwa Seo), 김동환(Dong-Hwan Kim), 한상민(Sangmin Han), 김관우(Kwanwoo Kim), 정성욱(Sungwook Chung), 유병우(Byeongwoo Yoo)
• Format: Journal Article
• Language: Korean
• Published: 대한조선학회 2024
• Subjects: 조선공학; Singular value decomposition(특이값 분해); Principal component analysis(주성분 분석); Marine engine(선박 기관)
• ISSN: 1225-1143; 2287-7355
• DOI: 10.3744/SNAK.2024.61.1.8

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.