access icon openaccess Data-aware monitoring method for fuel economy in ship-based CPS

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 27/10/2019, Accepted 30/01/2020, Revised 04/01/2020, Published 05/02/2020

With the acceleration of economic globalisation and the rapid development of network communication technology, remote monitoring and the management of ship fuel consumption have received extensive attention. Traditional fuel consumption monitoring methods are difficult to meet the growing management needs of the shipping industry due to problems such as large statistical errors and delayed information feedback. In order to better conduct energy management, equipment condition monitoring, and navigation analysis, the cyber-physical system (CPS) is deployed on ships to collect shipping data and communicate with remote monitoring centres. However, complex actual sailing conditions, sailing weather and other external factors tend to reduce the accuracy of fuel consumption data. In view of this challenge, a data-aware monitoring method for fuel consumption in ship-based CPS, named DMM, is proposed in this study. Technically, the fuel consumption index of ships is introduced firstly. Then, a fuel consumption model based on CPS is proposed, which improves the current fuel consumption model of the ship. Furthermore, the artificial neural network is employed to analyse a large amount of navigation data to get more accurate monitoring results of fuel consumption. Finally, experiments are conducted to verify the effectiveness of the authors’ proposed method.

Inspec keywords: cyber-physical systems; globalisation; energy consumption; ships; neural nets; computerised monitoring; energy conservation; fuel economy

Other keywords: fuel consumption data; ship-based CPS; fuel consumption index; shipping industry; traditional fuel consumption monitoring methods; fuel economy; sailing conditions; data-aware monitoring method; remote monitoring centres; equipment condition monitoring; ship fuel consumption; shipping data; navigation data; conduct energy management; network communication technology

Subjects: Neural computing techniques; Computerised instrumentation; Computing in other engineering fields; Computerised instrumentation

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