메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Ho-Min Park (Korea Maritime & Ocean University) Sang-Gyu Cheon (Panasia) Jae-Hoon Kim (Korea Maritime & Ocean University) Seong-Dae Lee (Korea Maritime & Ocean University)
저널정보
한국마린엔지니어링학회 Journal of Advanced Marine Engineering and Technology (JAMET) 한국마린엔지니어링학회지 제45권 제4호
발행연도
2021.8
수록면
222 - 230 (9page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
The demand for ecofriendly ships in shipbuilding and maritime industries is growing steadily. In response to strict environmental regulations on shipping by the International Maritime Organization, the demand for ship machinery and equipment that carry ecofriendly labels, including ballast water treatment systems (BWTS), is increasing. The BWTS involves early-stage equipment error and fault diagnostics, and its malfunction can have major cost and time consequences. This study expands on previous research on fault diagnosis using the SVM, a machine learning model. The two aspects of this expansion are an increased window size range used to generate the features and the introduction of several machine learning models. We used 47,435 sensor data points to compare and analyze the results and evaluate the classification accuracy by increasing the window size range to 10. We demonstrate that the previous model can be easily applied to other machine learning models and that the SVM model improves performance through feature generation. The F1 score of the random forest model with the highest performance score of 99.73% indicates potential for industrial applications if accompanied by expert monitoring and verification.

목차

Abstract
1. Introduction
2. Related Work
3. BWTS Fault Diagnosis Using Machine Learning Models
4. Experiment and Evaluation
5. Conclusion
References

참고문헌 (11)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0