Abstract
Shipbuilding and shipping areas are technically dependent to each other since shipping is the end user in the shipbuilding industry. One of the biggest maritime challenges arose due to regulations about emissions and has begun to be a worldwide concern. The maritime industry has enormous potential in terms of a reduction in energy use and emission supervision, especially with respect to existing ships. The specific challenges to shipping are energy inefficiency and emissions; vessel electrification, where sailing displacements and infrastructures permit; and vessel performance optimization. One of these challenges is addressed in this work: operational improvement in detection of the impact of fouling or opposite hull cleaning which enables greater efficiency and an increase in performance. In this paper, battery hybrid electric ship automation data are analysed using Curve Fitting and Detrended Fluctuation Analysis (DFA) in order to interpret the impact of fouling on ship energy performance degradation by continuous monitoring. Although this methodology is already used for analysing various time series data, DFA is a novel approach that is first time that applied to a marine based data where fouling contains marine biological properties. Application of these techniques reveal that in only 9 months fouling, performance degradation of case study ferry faced 6% of average speed loss.
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