Dimitrios Kaklis, Iraklis Varlamis, George Giannakopoulos, Constantine Spyropoulos, Takis J Varelas

Abstract:
Estimating the Fuel Oil Consumption (FOC) of a vessel is a critical task for the maritime industry, affecting route planning and the overall management of the vessel’s operation and maintenance. Consumption is strongly coupled with the operation of the Main Engine (ME), but also with the environmental conditions (i.e., weather, ocean-energy spectrum) and the hydrodynamic features (i.e., resistance, propulsion) of the vessel. Current research shows that a multitude of features collected either from the AIS (Automatic Identification System) or on-board sensors can assist to the continuous prediction of FOC. Even when a FOC estimation model is perfectly trained on a specific vessel, its performance may degrade over time, when new weather conditions apply or when the hydrodynamics of the vessel change over time, due to fouling, aging and negligent maintenance. This work presents an online learning framework that employs a custom encoding-decoding Neural Network scheme and real-time data from various on-board sensors, to appropriately update FOC estimation models. The model is able to adapt to newly acquired data using a temporally-aware batch scheme, that samples from the initial training set using a custom auto-encoder.