Ship performance monitoring using machine-learning

TLDR

Hydrodynamic performance of sea‑going ships changes over time due to fouling and anti‑fouling paint condition. The study seeks to assess this performance to accurately estimate power demand and fuel consumption for planned voyages using onboard in‑service data. Three ML models—NL‑PCR, NL‑PLSR, and probabilistic ANN—were calibrated on data from two sister ships to extract performance trends and predict changes across cleaning events. The ML predictions matched fouling‑friction‑coefficient estimates, with probabilistic ANN performing best, while NL‑PCR and NL‑PLSR also performed well, indicating simple methods can model hydrodynamic state with domain knowledge.

Abstract

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.