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Machine learning and cloud computing for remote monitoring of wave piercing catamarans: a case study using Matlab on Amazon web services

Citation

Shabani, B and Ali-Lavroff, J and Holloway, DS and Penev, S and Dessi, D and Thomas, G, Machine learning and cloud computing for remote monitoring of wave piercing catamarans: a case study using Matlab on Amazon web services, Proceedings of the Smart Ship Technology 2020 International Conference, 14-15 October 2020, online, pp. 83-94. ISBN 9781911649106 (2020) [Refereed Conference Paper]

Copyright Statement

Copyright 2020 The Royal Institution of Naval Architects

Official URL: https://www.rina.org.uk/Smart_Ship_Technology_2020...

Abstract

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high-speed catamarans operating in moderate to large waves. Although developing a hull monitoring system according to classification guidelines for such vessels is broadly acceptable, the data processing requirements for outputs such as rainflow counting, filtering, probability distribution, fatigue damage estimation and warning due to slamming can be as sophisticated to implement as the system components themselves. Advanced analytics such as machine learning and deep learning data pipelines will also create more complexities for such systems, if included. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service.

Item Details

Item Type:Refereed Conference Paper
Keywords:remote monitoring, catamarans, machine learning, cloud computing
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Ship and platform structures (incl. maritime hydrodynamics)
Objective Division:Transport
Objective Group:Water transport
Objective Field:Domestic passenger water transport (e.g. ferries)
UTAS Author:Shabani, B (Dr Babak Shabani)
UTAS Author:Ali-Lavroff, J (Dr Jason Ali-Lavroff)
UTAS Author:Holloway, DS (Mr Donald Holloway)
ID Code:141484
Year Published:2020
Deposited By:Engineering
Deposited On:2020-10-22
Last Modified:2021-02-17
Downloads:0

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