Authors:
Iori Oki
1
;
Seiji Yamada
2
and
Takashi Onoda
1
Affiliations:
1
Aoyama Gakuin University School of Science and Engineering, 5-10-1 Huchinobe, Chuo, Sagamihara, Kanagawa, Japan
;
2
National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda, Tokyo, Japan
Keyword(s):
Anomaly Detection, Outlier Detection, Explainable AI, Explainability, One-Class Support Vector Machine, SHAP.
Abstract:
The equipment onboard merchant vessels are essential for safe navigation. If an equipment malfunction occurs during a voyage, it is difficult to repair it with the same speed and accuracy as on land. Therefore, it is important to It is required to be able to repair and replace the equipment with a margin of time by detecting the signs of anomalies. In this paper, we present the results of detecting signs of anomalies from various sensor data collected using One-Class SVM. It also shows the results of interpreting the signs of anomalies and detected locations using SHAP. The results show that the proposed method can detect signs of anomalies at a point about one month before the conventional method. Therefore, the proposed method is shown to be potentially useful for the maintenance of equipment on merchant vessels.