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Abstract

An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering currenttechnology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has beendeveloped and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses.Parallel to that, an existing dataset of a geometrically similar vessel (Hull 061) was analysed using unsupervised and supervised learning models;these were found to be beneficial for the classification of bow entry events according to the kinematic parameters. A comparison of differentalgorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition,using empirical probability distributions, the likelihood of wet-deck slamming was estimated given vertical bow acceleration thresholds.

Item Type:	Conference Publication
Authors/Creators:	Shabani, B and Lavroff, J and Holloway, DS and Penev, S and Dessi, D and Thomas, G
Keywords:	remote monitoring, catamarans, machine learning, slamming
Journal or Publication Title:	Proceedings of the International Conference on Marine Industry 4.0
Publisher:	The Royal Institution of Naval Architects
Copyright Information:	Copyright 2019 The Royal Institution of Naval Architects
Related URLs:	Google Scholar: Lavroff, J UTAS Profile: Lavroff, J Google Scholar: Holloway, DS UTAS Profile: Holloway, DS
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