Ship As a Wave Buoy: Estimating Relative Wave Direction From In-Service Ship Motion Measurements Using Machine Learning

Abstract

For operations at sea it is important to have a good estimate of the current local sea state. Often, sea state information comes from wave buoys or weather forecasts. Sometimes wave radars are used. These sources are not always available or reliable. Being able to reliably use ship motions to estimate sea state characteristics reduces the dependency on external and/or expensive sources. In this paper, we present a method to estimate sea state characteristics from time series of 6-DOF ship motions using machine learning. The available data consists of ship motion and wave scanning radar measurements recorded for a period of two years on a frigate type vessel. The research focused on estimating the relative wave direction, since this is most difficult to estimate using traditional methods. Time series are well suited as input, since the phase differences between motion signals hold the information relevant for this case. This type of input data requires machine learning algorithms that can capture both the relation between the input channels and the time dependence. To this end, convolutional neural networks (CNN) and recurrent neural networks (RNN) are adopted in this study for multivariate time series regression. The results show that the estimation of the relative wave direction is acceptable, assuming that the data set is large enough and covers enough sea states. Investigating the chronological properties of the data set, it turned out that this is not yet the case. The paper will include discussions on how to interpret the results and how to treat temporal data in a more general sense.