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Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high-speed catamarans operating in moderate to large waves. 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:	Refereed Conference Paper
Keywords:	machine learning, cloud computing, remote monitoring, catamaran
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Artificial intelligence not elsewhere classified
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 (Associate Professor Damien Holloway)
ID Code:	141663
Year Published:	2020
Funding Support:	Australian Research Council (LP170100555)
Deposited By:	Engineering
Deposited On:	2020-11-08
Last Modified:	2020-12-08
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