从拖船捕获和分析实时数据

Serena Lim, K. Pazouki, A. Murphy, Ben Zhang
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摘要

航运业的整体能源管理包括可靠的数据收集、系统的处理和智能的分析。数字化时代允许传感器技术在船上使用,将不同形式的信号转换成可以方便地导出进行进一步处理的数字格式。当船舶在恶劣条件下航行时,适当的传感器选择对于确保连续的数据收集非常重要。然而,如果没有适当的处理,这将导致大数据集的收集,但不会产生对行业有益的有用情报。采用数字和计算机技术,可以进行下一阶段的快速数据处理。这促进了大数据分析领域的发展,这是包括海运业在内的许多技术部门面临的一个问题。庞大的数据库通常没有明确的目标或合适的用途。数据处理需要工程知识,以确保对原始数据应用合适的过滤器。这种系统的数据处理导致了实时数据显示的透明度,并有助于预测分析。此外,当与其他外部数据(如天气信息)相结合时,生成的一系列原始数据提供了一个反映船舶真实情况的丰富数据库。随后的处理将为优化操作提供改进的决策工具。这些进步为不同的市场分析和新知识的产生打开了大门。本文强调了传感器安装所需的关键步骤和挑战,以获得准确的数据,其次是数据的预处理和后处理,以产生知识。有了这些,大数据现在可以提供信息,揭示有关船舶操作、机械诊断和节能船队管理的隐藏模式和趋势。在北海的一艘拖船上进行了一个案例研究,首先展示了对收集到的原始数据的信心,其次展示了对有用信息的系统过滤、汇总和显示。
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Capturing and Analysing Real-Time Data From Tugs
Holistic energy management in the shipping industry involves reliable data collection, systematic processing and smart analysis. The era of digitisation allows sensor technology to be used on-board vessels, converting different forms of signal into a digital format that can be exported conveniently for further processing. Appropriate sensor selection is important to ensure continuous data collection when vessels sail through harsh conditions. However, without proper processing, this leads to the collection of big data sets but without resulting useful intelligence that benefits the industry. The adoption of digital and computer technology, allows the next phase of fast data processing. This contributes to the growing area of big data analysis, which is now a problem for many technological sectors, including the maritime industry. Enormous databases are often stored without clear goals or suitable uses. Processing of data requires engineering knowledge to ensure suitable filters are applied to raw data. This systematic processing of data leads to transparency in real time data display and contributes to predictive analysis. In addition, the generation of series of raw data when coupled with other external data such as weather information provides a rich database that reflects the true scenario of the vessel. Subsequent processing will then provide improved decision making tools for optimal operations. These advances open the door for different market analyses and the generation of new knowledge. This paper highlights the crucial steps needed and the challenges of sensor installation to obtain accurate data, followed by pre and post processing of data to generate knowledge. With this, big data can now provide information and reveal hidden patterns and trends regarding vessel operations, machinery diagnostics and energy efficient fleet management. A case study was carried out on a tug boat that operates in the North Sea, firstly to demonstrate confidence in the raw data collected and secondly to demonstrate the systematic filtration, aggregation and display of useful information.
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