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http://dx.doi.org/10.36498/kbigdt.2022.7.1.125

Outlier Detection and Labeling of Ship Main Engine using LSTM-AutoEncoder  

Dohee Kim (부산대학교 산업공학과 산업데이터공학융합전공)
Yeongjae Han (부산대학교 산업공학과 산업데이터공학융합전공)
Hyemee Kim (부산대학교 산업공학과 산업데이터공학융합전공)
Seong-Phil Kang (랩오투원)
Ki-Hun Kim (부산대학교 산업공학과 산업데이터공학융합전공)
Hyerim Bae (부산대학교 산업공학과 산업데이터공학융합전공)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 125-137 More about this Journal
Abstract
The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.
Keywords
Ship; Outlier detection; LSTM-AutoEncoder; Clustering; Big-data;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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