Multi-Sensor Data Fusion for the Vessel Trim Analyzer and Optimization Platform

Filippopoulos, Ioannis; Lajic, Zoran; Georgios, Mitsopoulos; Senteris, Alexandros; Pearson, Margot

doi:10.1109/icsrs48664.2019.8987689

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Modern vessels utilize onboard sensors and data acquisition systems to collect ship performance and navigation parameters [ 1 ]. The explosion of the sensor data enables one to apply machine learning techniques to the data to extract knowledge through data analysis [ 2 ] and even trigger interesting data-driven applications, including fuel consumption prediction [ 3 , 4 ], prediction of speed loss [ 5 ], propulsion power prediction [ 6 ], weather routing [ 7 ], development of monitoring systems [ 8 ] and enhanced inspection [ 9 ], energy efficiency analysis [ 10 ], trim optimization [ 11 ], predictive maintenance [ 12 ], data-driven prognostics [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

Kim¹,

Antariksa

Handayani

et al. 2021

Sensors

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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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