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

Abstract 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.

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