Anomaly Detection in Vessel Sensors Data with Unsupervised Learning Technique

被引：2

作者：

Handayani, Melia Putri ^{[1
]}

Antariksa, Gian ^{[1
]}

Lee, Jihwan ^{[1
]}

机构：

[1] Pukyong Natl Univ, Dept Ind Data Engn Ind Data Sci & Engn, Busan, South Korea

来源：

2021 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | 2021年

关键词：

Anomaly Detection; Isolation Forest; t-SNE; Machine Learning; Unsupervised Learning; Vessels Sensors;

D O I：

10.1109/ICEIC51217.2021.9369822

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In a large ship or vessel, there are a lot of sensors forming a system that is used to indicate the engine status. It is critical for the system to be able to detect any anomaly that may cause engine failures. By detecting the anomaly of the data, maintenance for the sensors can be well-recommended and this also contributes to the reduction of maintenance costs. In this research, a collection of sensor data from vessels was analyzed using an Isolation Forest to detect the anomaly of the data. To reduce the dimensionality of the data, the t-SNE was adopted.

引用

页数：6

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