| Summary: | Shipping has been recognized as the most efficient mode of transport, carrying over 80% of international trade volume. The shipping industry is at the beginning of one of its greatest energy and technology transition driven by decarbonization drivers in terms of stringent emission regulations and commercial pressure. Digitalization can enable the transition by leveraging Machine Learning (ML) and Data Analytics (DA) techniques with a focus on enhancing energy efficiency during ship operations. Furthermore, such a transition is expected to exert tremendous impacts on the life-cycle costs of ships’ assets and systems. Under the scope of maritime decarbonization, the main aim of this thesis is to develop an integrated data analytics framework for enhancing the environmental and life-cycle economic performance in the shipping industry. In order to achieve the stated aim, a set of objectives are specified under two distinct frameworks which are developed in individual methodologies and applied in unique case studies illustrating their effectiveness in the respective objectives. Firstly, an advanced data analytics framework (ADAF) is proposed to quantify the operational performance of a bulk carrier on a local scale with respect to its operational conditions. The ADAF includes appropriate data analytics along with domain knowledge for the detection of data anomalies, the investigation of the ship’s localized operational conditions via data clustering, the identification of the relative correlations among the investigated parameters and the quantification of the ship’s performance in each of the respective conditions (i.e., engine modes and trim-draft modes). Given the data set used for the implementation of the ADAF, a ship performance index (SPI) is derived to find the best performance trim-draft mode under the engine modes of the ship. The findings generated from the ADAF add to the growing field of fault diagnostics, ship performance and condition monitoring in the maritime research domain and are particularly ...
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