Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data ins...

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Bibliographic Details
Main Authors: Donghyun Kim, Gian Antariksa, Melia Putri Handayani, Sangbong Lee, Jihwan Lee
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Sensors
Subjects:
explainable AI
anomaly detection
isolation forest
Shapley Additive exPlanations
SHAP
clustering
Online Access:https://www.mdpi.com/1424-8220/21/15/5200

Internet

https://www.mdpi.com/1424-8220/21/15/5200

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