This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

서 론
재료 및 방법
결과 및 고찰
    앙상블 기법 적용 결과
    군집분석을 통한 이상치 유형 분류
    군집간 특성비교를 통한 이상치 원인분석
결 론
References

• 김동현(한국조선해양기자재연구원) | Dong-Hyun KIM (Autonomous Ship Technology Center, Korea Marine Equipment Research Institute)
• 이지환(부경대학교 시스템경영공학부) | Ji-Hwan LEE (Division of Systems Management and Engineering, Pukyong National University) Corresponding author
• 이상봉(랩오투원) | Sang-Bong LEE (LAB021)
• 정봉규(경상대학교 해양산업연구소) | Bong-Kyu JUNG (Marine Industry Research Center, Gyeongsang National University)