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