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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, GAn 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 EngineeringVolume
163Issue
A3Pagination
A15-A30ISSN
1479-8751Department/School
School of EngineeringPublisher
Royal Institution of Naval ArchitectsPlace of publication
United KingdomRights statement
©2021: The Royal Institution of Naval ArchitectsRepository Status
- Restricted