This project contributes statistics and machine learning methodology for exploiting new high-dimensional and high-frequency sensor data on board vessels at sea, to improve marine safety, efficiency and availability. Shipping is and will be one of the leading Norwegian industries. This project develops innovative solutions for networks of sensors, programmed to measure many variables which are known to be important in describing the status of various types of machineries and components of a vessel. ABB has unique access to such sensor data, which are for the first time streamed from vessels in operation to ABB?s control centre in Norway. The purpose of the thesis is to investigate how these data can be used to generate deep knowledge about processes and dynamics, which lead to abnormal operations or even faults on vessels. This thesis focuses on two challeges: (i) Data management of huge data time series of different scale, collected over longer periods and on many vessels: how to organise, match and annotate such data in order to be able to investigate patterns and perform analysis? (ii) High dimensional, scalable statistical methods are needed, together with efficient machine learning approaches, in order to detect abnormalities or faults appearing in some components and in some scales, as rapidly as possible. The time series are high dimensional, with hundreds of sensors on each vessel and the signal of abnormality can reside in any of the stochastic multivariate structures of the time series. In addition, multivariate dependence can be at various lags. The series need to be analysed for surprising and abnormal behaviour. The sample frequency of the sensors varies very much. We will study how to handle these variable sample frequencies without resorting on massive missing values, both in storing and in analysing. Real time sensor data lead to huge data sets, which challenge storage and algorithms. All our methods and algorithms must scale computationally.
Project leader: Morten Stakkeland
Institution: ABB AS