Estimating the effect of biofouling on ship shaft power based on sensor measurements

Marine biofouling on a ship's hull and propeller increases the resistance of the ship moving through water and reduces the propulsion efficiency of the ship. Estimating the effect of fouling is difficult, as the biomass is rarely measured. In this paper, we present a new data-driven model for the total shaft power use of a large containership, in order to estimate the unobserved effect of fouling. Due to the limitations of both physical models and machine learning models, we develop a Bayesian generalized additive model for our purpose. We discuss issues of representative training data for the model. Further, we subset and subsample the data to a representative sample. Models are compared by out-of-sample predictive quality, physical appropriateness, and through autocorrelation of residuals. The Bayesian generalized additive model combined with computational inference using integrated nested Laplace approximations gives a robust estimate of the biofouling effect over time. It also allows a decomposition of the total shaft power use into effects of speed, weather, and other conditions. This model can be used to understand the effectiveness and timing of different hull and propeller treatments.