Data Processing Framework for Ship Performance Analysis

TL;DR

This paper introduces a semi-automatic data processing framework designed to efficiently handle and analyze ship performance data from various sources, addressing common data issues for hydrodynamic analysis.

Contribution

The work presents a novel, streamlined framework capable of processing diverse ship data sources with steps like interpolation, validation, and outlier detection, enhancing ship performance analysis.

Findings

Framework effectively processes data from multiple sources.

Improves data quality for hydrodynamic performance analysis.

Facilitates faster and more reliable ship performance assessments.

Abstract

The hydrodynamic performance of a sea-going ship can be analysed using the data obtained from the ship. Such data can be gathered from different sources, like onboard recorded in-service data, AIS data, and noon reports. Each of these sources is known to have their inherent problems. The current work gives a brief introduction to these data sources as well as the common problems associated with them, along with some examples. In order to resolve most of these problems, a streamlined semi-automatic data processing framework for fast data processing is developed and presented here. The data processing framework can be used to process the data obtained from any of the above three mentioned sources. The framework incorporates processing steps like interpolating weather hindcast (metocean) data to ship's location in time, deriving additional features, validating data, estimating resistance components, data cleaning, and outlier detection. A brief description of each of the processing steps is provided with examples from existing datasets. The processed data can be further used to analyse the hydrodynamic performance of a ship.