Machine Learning based Data Handling Framework for Ship Energy Efficiency
Journal article, Peer reviewed
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Appropriate navigation strategies should be developed to overcome the current shipping industrial challenges under emission-control-based energy efficiency measures. Effective navigation strategies should be based on accurate ship performance and navigation information; therefore, various onboard data handling systems are installed on ships to collect large-scale datasets. Ship performance and navigation data that are collected to develop such navigation strategies can be an integrated part of the ship energy efficiency management plan (SEEMP). Hence, the SEEMP with various navigation strategies can play an important part of e-navigation under modern integrated bridge systems. This study proposes a machine-intelligence-based data handling framework for ship performance and navigation data to improve the quality of the respective navigation strategies. The prosed framework is divided into two main sections of pre and post processing. The data pre-processing is an onboard application that consists of sensor faults detection, data classification, and data compression steps. The data post processing is a shore-based application (i.e., in data centers) and that consists of data expansion, integrity verification, and data regression steps. Finally, a ship performance and navigation dataset of a selected vessel is analyzed through the proposed framework and successful results are presented in this study.