[1F5-GS-10] AI application: anomaly detection 1

[1F5-GS-10-03] Comparison of MT Method and OCSVM in Detecting Predictive Abnormalities of Navigational and Radio Equipment onboard Merchant Ships

〇Iori Oki¹, Takashi Onoda¹, Takahiro Nishigaki¹, Naoya Hashimoto², Yoshiaki Moritoki³ (1. Aoyama Gakuin University, 2. FURUNO ELECTRIC CO., 2. LINCREA CORPORATION)

Keywords:Anomaly detection, Machine Learning

This study targets the prediction of anomalies in navigation and radio equipment onboard merchant ships. These devices are used for voice and other communications with business management centers on land via satellite. However, as these devices deteriorate over time or become overloaded, they can cause abnormalities that prevent voice communication. The objective of this research was to be able to automatically identify the signs of such abnormalities based on the characteristics of the data, and to prompt the replacement of the equipment. In this experiment, the MT method and OCSVM were used with voltage values and CPU temperature as features. Results of the experiment, the MT method was not able to find all the points that the experts wanted to identify as predictive signs of anomalies, but the OCSVM was able to detect all the points.