Advanced data analytics for ship performance monitoring under localized operational conditions

Improving the operational energy efficiency of existing ships is attracting considerable interests to reduce the environmental footprint due to air emissions. As the shipping industry is entering into Shipping 4.0 with digitalization as a disruptive force, an intriguing area in the field of ship’s operational energy efficiency is big data analytics. This paper proposes a big data analytics framework for ship performance monitoring under localized operational conditions with the help of appropriate data analytics together with domain knowledge. The proposed framework is showcased through a data set obtained from a bulk carrier pertaining the detection of data anomalies, the investigation of the ship’s localized operational conditions, the identification of the relative correlations among parameters and the quantification of the ship’s performance in each of the respective conditions. The novelty of this study is to provide a KPI (i.e. key performance indicator) for ship performance quantification in order to identify the best performance trim-draft mode under the engine modes of the case study ship. The proposed framework has the features to serve as an operational energy efficiency measure to provide data quality evaluation and decision support for ship performance monitoring that is of value for both ship operators and decision-makers.