Statistical Monitoring and Clustering of Ship's Time Series

Monitoring and clustering of ship and main engine motions are urgent problems in order to realize energy saving navigation. In this paper, authors represent their motions by statistical autoregressive model using minimum AIC procedure. The modelling proceeds applying locally stationary fitting procedure. The watch officers and operators can automatically detect the change of ship's states by monitoring the spectra gained from the fitted model and detect the outlier, missing values of the data and predict the motions using Kalman filtering procedure. The most important feature of this system is to automatically cluster the ship's time series and save the results to the database. The watch officers and operators can analyse the past ship's and main engine's states and determine the next operating method of the navigation including the gain scheduling the ship's control system, for examples, autopilot, the tracking system, ship's governor system and so on.