A novelty detection approach to diagnosing hull and propeller fouling

Abstract

Hull and propeller performance have a primary role in overall vessel efficiency. Vessel fouling is a common phenomenon where undesirable substances attach or grow on the ship hull. A clear understanding of the extent of the degradation of the hull will allow better management of assets and prediction of the best time for dry docking and hull maintenance work. In this paper, the authors investigate the problems of predicting the hull condition in real operations based on data measured by the on-board systems. The proposed solution uses an unsupervised Machine Learning (ML) modelling technique to eliminate the need for collecting labeled data related to the hull and propeller fouling condition. Two anomaly detection methods based on Support Vector Machines and k-nearest neighbour have been applied to predict the hull condition using the available parameters measured on-board. Data from the Research Vessel The Princess Royal has been exploited to show the effectiveness of the proposed methods and to benchmark them in a realistic maritime application.