ArticleSensors (Basel, Switzerland)2021
Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- Deep learning framework for predicting measurement error drift in smart meter sensors under harsh coastal environments.PloS one · 2026Article
- Multivariate Gas Sensor E-Nose System with PARAFAC and Machine Learning Modeling for Quantifying and Classifying the Impact of Fishing Gears.Sensors (Basel, Switzerland) · 2025Article
- A Method for the Intelligent Identification of the Operating Conditions of Deep-Sea Submersibles and Its Application.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning Based Prediction of Postoperative Acute Kidney Injury Risk in Coronary Artery Bypass Grafting Patients.Clinical interventions in aging · 2025Article
- SiteFerret: Beyond Simple Pocket Identification in Proteins.Journal of chemical theory and computation · 2023Article
- Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model.Sensors (Basel, Switzerland) · 2023Article
- Exploring the Quality of Dynamic Open Government Data Using Statistical and Machine Learning Methods.Sensors (Basel, Switzerland) · 2022Article
- Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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 instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.