WEBINAR - Ship performance monitoring using machine-learning

20.May.2022

WEBINAR: Ship performance monitoring using machine-learning 

DateFriday, May 20th, 2022

Time09:00 – 10:00

Place: Microsoft Teams

Registration: by accepting Outlook meeting invitation; meeting open to all SFI Smart Maritime participants.

Organiser: NTNU

 

Dr Prateek Gupta will present his latest publication and final results of his PhD work.

Gupta, P., Rasheed, A., & Steen, S. (2022). Ship performance monitoring using machine-learning. Ocean Engineering, 254, 111094.

The hydrodynamic performance of a sea-going ship varies over its lifespan due to factors like marine fouling and the condition of the anti-fouling paint system. In order to accurately estimate the power demand and fuel consumption for a planned voyage, it is important to assess the hydrodynamic performance of the ship. The current work uses machine-learning (ML) methods to estimate the hydrodynamic performance of a ship using the onboard recorded in-service data. Three ML methods, NL-PCR, NL-PLSR and probabilistic ANN, are calibrated using the data from two sister ships. The calibrated models are used to extract the varying trend in ship’s hydrodynamic performance over time and predict the change in performance through several propeller and hull cleaning events. The predicted change in performance is compared with the corresponding values estimated using the fouling friction coefficient (𝛥𝐶𝐹 ). The ML methods are found to be performing well while modeling the hydrodynamic state of the ships with probabilistic ANN model performing the best, but the results from NL-PCR and NL-PLSR are not far behind, indicating that it may be possible to use simple methods to solve such problems with the help of domain knowledge.

Dr Gupta has been PhD Candidate at SFI Smart Maritime (WP2) from 2018 to 2022, working on Ship Performance Monitoring using In-service Measurements and Big Data Analysis Methods, which Professor Sverre Steen, NTNU as Supervisor. He defended his PhD thesis last March. 

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