天津科技

天津科技 ›› 2025, Vol. 52 ›› Issue (04): 28-32+35.

• 基础研究 • 上一篇    下一篇

基于数据驱动算法的船舶主机运行监测及健康评估

王琰, 周毅, 李萌, 蒙学昊, 罗恒安   

  1. 中海油能源发展股份有限公司采油服务分公司 天津 300452
  • 收稿日期:2025-03-03 出版日期:2025-04-25 发布日期:2026-01-06

Monitoring and health assessment of ship main engine operation based on data-driven algorithms

WANG Yan, ZHOU Yi, LI Meng, MENG Xuehao, LUO Heng’an   

  1. CNOOC Energy Technology & Services-Oil Production Services Co.,Tianjin 300452,China
  • Received:2025-03-03 Online:2025-04-25 Published:2026-01-06

摘要： 对船舶主机的健康实时监测问题进行研究,突破传统的机舱综合报警监测系统（AMS）监测模式采用连续时间的多维数据,以马氏距离、孤立森林、密度聚类算法（DBSCAN）、单类支持向量机（OCSVM）4类异常检测算法对实船数据进行实验验证,基于每个算法在各个场景下的实际表现,通过与出现异常的数据点进行对照,4类算法在异常检测的灵敏度、误判、遗漏指标方面均有较好的表现。

关键词: 船舶主机健康, 数据异常监测, 马氏距离, 孤立森林, DBSCAN, OCSVM

Abstract: The real-time monitoring of the health of the ship main engine is studied,and the traditional AMS alarm system monitoring method is made by using multi-dimensional data of continuous time. The real ship data is experimented by four types of anomaly detection algorithms：Mahalanobis distance,Isolation Forest,DBSCAN,and OCSVM,and the actual performance of each algorithm in each scenario is obtained. By comparing with the actual abnormal data points,the four types of algorithms have good performance in the sensitivity,false judgment and missing indicators of anomaly detection.

Key words: health of ship main engine, data anomaly monitoring, Mahalanobis distance, Isolation Forest, DBSCAN, OCSVM

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