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.

Item Type:	Refereed Article
Keywords:	high-speed catamaran, machine learning, slamming
Research Division:	Engineering
Research Group:	Mechanical engineering
Research Field:	Mechanical engineering not elsewhere classified
Objective Division:	Transport
Objective Group:	Water transport
Objective Field:	International passenger water transport (e.g. passenger ships)
UTAS Author:	Shabani, B (Dr Babak Shabani)
UTAS Author:	Ali-Lavroff, J (Dr Jason Ali-Lavroff)
UTAS Author:	Holloway, DS (Associate Professor Damien Holloway)
ID Code:	148787
Year Published:	2021
Funding Support:	Australian Research Council (LP170100555)
Deposited By:	Engineering
Deposited On:	2022-02-09
Last Modified:	2022-03-01
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