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Data Driven Decision Making in Planning the Maintenance Activities of Off-shore Wind Energy

机译：规划离岸风能维护活动的数据驱动决策

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Planning and scheduling for wind farms play a critical role in the costs of maintenance. The use and analysis of field data or so-called Product Use Information (PUI) to improve maintenance activities and to reduce the costs has gained attention in the recent years. The product use data consist of sources such as measure of sensors on the turbines, the alarms information or signals from the condition monitoring, Supervisory Control and Data Acquisition (SCADA) systems, which are currently used in maintenance activities. However, those data have the potential to offer alternative solutions to improve processes and provide better decisions, by transforming them into actionable knowledge. In order to make the right decision it is important to understand, which PUI data source and which data analysis methods, are suitable for what kind of decision making task. The aim of this study is to discover, how analysis of PUI can help in the maintenance processes of off-shore wind power. The techniques from the field of big data analytics for analyzing the PUI are here addressed. The results of this study contain suggestions on the basis of algorithms of data analytics, suitable for each decision type.
机译：为风电场规划和安排在维护成本中发挥着关键作用。现场数据或所谓产品使用信息（PUI）的使用和分析，以改善维护活动，并在近年来降低成本。产品使用数据包括诸如涡轮机上的传感器的测量来源，警报信息或来自条件监测，监控和数据采集（SCADA）系统的信号，目前用于维护活动。但是，这些数据有可能提供替代解决方案来改进流程并通过将其转化为可操作的知识来提供更好的决策。为了做出正确的决定，了解哪些PUI数据源以及哪种数据分析方法适用于什么样的决策任务。本研究的目的是发现，PUI的分析如何有助于岸上风电的维护过程。这里解决了分析PUI的大数据分析领域的技术。本研究的结果包含基于数据分析的算法的建议，适用于每个决策类型。

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