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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

Shabani, B ORCID: 0000-0002-2910-962X, Ali-Lavroff, J ORCID: 0000-0001-5262-8666, Holloway, DS, Penev, S, Dessi, D and Thomas, G 2020 , 'Machine learning and cloud computing for remote monitoring of wave piercing catamarans: a case study using Matlab on Amazon web services', in Proceedings of the Smart Ship Technology 2020 International Conference , The Royal Institution of Naval Architects, London, pp. 83-94 .

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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 Type: Conference Publication
Authors/Creators:Shabani, B and Ali-Lavroff, J and Holloway, DS and Penev, S and Dessi, D and Thomas, G
Keywords: remote monitoring, catamarans, machine learning, cloud computing
Journal or Publication Title: Proceedings of the Smart Ship Technology 2020 International Conference
Publisher: The Royal Institution of Naval Architects
Copyright Information:

Copyright 2020 The Royal Institution of Naval Architects

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