Predicting motion sickness for crew transfer vessels using modelling and date mining techniques

Abstract

In recent years, the offshore wind industry has grown significantly. The wind turbines are constructed in wind farms, and are serviced with relatively small crew transfer vessels. These vessels transport repair crews from the mainland to the farms and back within a day. One of the big challenges is that these transits can cause motion sickness. If one crew member gets sick, the vessel is legally required to return to the harbour, without performing the repair. It is of interest to predict motion sickness, since both a failed repair and a broken wind turbines that could have been repaired are costly. This can be done by predicting the motion sickness incidence (MSI), which is an objective measure for motion sickness.
The goal of this thesis is to predict MSI using the wave conditions measured in or near the wind farms, by means of an analytical and numerical approach using machine learning. Testing is done on two sites: the Greater Gabbard wind farm and the Westermost Rough wind farm. The analytical approach relates the recorded wave spectrum to the motion spectrum of the vessel, but proves infeasible as a stand-alone method. Gaussian regression, tree ensemble regression and neural networks with Bayesian regularization and backpropagation are the best performing machine learning techniques for MSI prediction. We conclude that the methods from this thesis have sufficient accuracy, by comparing our results with those from Orsted, whose model is already successfully deployed.