Toggle Main Menu Toggle Search

Open Access padlockePrints

Ship fuel consumption monitoring and fault detection via partial least squares and control charts of navigation data

Lookup NU author(s): Dr Shirley ColemanORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


New regulations in the shipping sector aim to give greater transparency to operations and public access to CO2 emissions data. EU regulation 2015/757 became mandatory in January 2018 and urges shipping companies to set up systems for daily monitoring, reporting and verification (MRV) of emissions for individual ships. Manual acquisition and handling of emissions data may be allowed (e.g. bunker fuel delivery note, bunker fuel tank monitoring), but is adversely affected by uncertainty due to human intervention and will eventually be unusable for monitoring purposes. However, the massive amounts of navigational data acquired by multi-sensor systems installed on-board modern ships have great potential to aid compliance with regulations but their use is hampered by the lack of effective analytical methods in maritime literature. This work demonstrates a statistical framework and automatic reporting system for fuel consumption monitoring that addresses the MRV requirements needed to comply with the regulations. The framework has been applied to the Grimaldi Group’s Ro-Ro Pax cruise ships and is shown, in addition, to be capable of supporting fault detection as well as verifying CO2 savings achieved after energy efficiency initiatives.

Publication metadata

Author(s): Capezza C, Coleman SY, Lepore A, Palumbo B, Vitiello L

Publication type: Article

Publication status: Published

Journal: Transportation Research Part D: Transport and Environment

Year: 2019

Volume: 67

Pages: 375-387

Print publication date: 01/02/2019

Online publication date: 22/12/2018

Acceptance date: 14/11/2018

Date deposited: 15/01/2019

ISSN (print): 1361-9209

ISSN (electronic): 1879-2340

Publisher: Elsevier


DOI: 10.1016/j.trd.2018.11.009


Altmetrics provided by Altmetric