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http://dx.doi.org/10.3796/KSFOT.2020.56.4.384

Outlier detection of main engine data of a ship using ensemble method  

KIM, Dong-Hyun (Autonomous Ship Technology Center, Korea Marine Equipment Research Institute)
LEE, Ji-Hwan (Division of Systems Management and Engineering, Pukyong National University)
LEE, Sang-Bong (LAB021)
JUNG, Bong-Kyu (Marine Industry Research Center, Gyeongsang National University)
Publication Information
Journal of the Korean Society of Fisheries and Ocean Technology / v.56, no.4, 2020 , pp. 384-394 More about this Journal
Abstract
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.
Keywords
Ship; Main Engine; Outlier detection; Predictive maintenance; Big data;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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