Big Data Analytics As a Tool to Monitor Hydrodynamic Performance of a Ship
A modern ship is fitted with numerous sensors and Data Acquisition Systems (DAQs) each of which can be viewed as a data collection source node. These source nodes transfer data to one another and to one or many centralized systems. The centralized systems or data interpreter nodes can be physically...
| Published in: | Volume 7A: Ocean Engineering |
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| Main Authors: | , , |
| Format: | Book Part |
| Language: | English |
| Published: |
ASME
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/11250/2978778 https://doi.org/10.1115/OMAE2019-95815 |
| Summary: | A modern ship is fitted with numerous sensors and Data Acquisition Systems (DAQs) each of which can be viewed as a data collection source node. These source nodes transfer data to one another and to one or many centralized systems. The centralized systems or data interpreter nodes can be physically located onboard the vessel or onshore at the shipping data control center. The main purpose of a data interpreter node is to assimilate the collected data and present or relay it in a concise manner. The interpreted data can further be visualized and used as an integral part of a monitoring and decision support system. This paper presents a simple data processing framework based on big data analytics. The framework uses Principal Component Analysis (PCA) as a tool to process data gathered through in-service measurements onboard a ship during various operational conditions. Weather hindcast data is obtained from various sources to account for environmental loads on the ship. The proposed framework reduces the dimensionality of high dimensional data and determines the correlation between data variables. The accuracy of the model is evaluated based on the data recorded during the voyage of a ship. publishedVersion |
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