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Using remote monitoring and machine learning to classify slam events of wave piercing catamarans

journal contribution
posted on 2023-05-21, 05:41 authored by Babak Shabani, Jason Ali-LavroffJason Ali-Lavroff, Damien HollowayDamien Holloway, Penev, S, Dessi, D, Thomas, G
An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed 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 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 key kinematic parameters. A comparison of different algorithms 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 a vertical bow acceleration threshold of 1 in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.

Funding

Australian Research Council

Incat Tasmania Pty Ltd

History

Publication title

International Journal of Maritime Engineering

Volume

163

Issue

A3

Pagination

A15-A30

ISSN

1479-8751

Department/School

School of Engineering

Publisher

Royal Institution of Naval Architects

Place of publication

United Kingdom

Rights statement

©2021: The Royal Institution of Naval Architects

Repository Status

  • Restricted

Socio-economic Objectives

International passenger water transport (e.g. passenger ships)

Usage metrics

    University Of Tasmania

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