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Using remote monitoring and machine learning to classify slam events of wave piercing catamarans
Citation
Shabani, B and Ali-Lavroff, J and Holloway, DS and Penev, S and Dessi, D and Thomas, G, Using remote monitoring and machine learning to classify slam events of wave piercing catamarans, International Journal of Maritime Engineering, 163, (A3) pp. A15-A30. ISSN 1479-8751 (2021) [Refereed Article]
Copyright Statement
©2021: The Royal Institution of Naval Architects
DOI: doi:10.5750/ijme.v163iA3.797
Abstract
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 Details
Item Type: | Refereed Article |
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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 |
Downloads: | 0 |
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