Big Data Analytics in the Maintenance of Off-Shore Wind Turbines: A Study on Data Characteristics

  • Elaheh Gholamzadeh Nabati
  • Klaus Dieter Thoben
Conference paper
Part of the Lecture Notes in Logistics book series (LNLO)


The aim of this study is to discuss the characteristics of the input data to the data analytical algorithms of a predictive maintenance system, from the viewpoint of big data technology. The discussed application is for the maintenance of off-shore wind turbines. The maintenance of off-shore wind turbines is an expensive and sensitive task. Therefore, making decision for planning and scheduling of maintenance in a wind farm (which is made by the operating company of a wind farm) is important and plays a critical role in the cost of maintenance. In this paper, the current state of the art for big data technology in the maintenance of off-shore wind turbines is presented. The dimensions of big data analytics and the technical requirements of data for the use of this technology in the maintenance of off-shore wind turbines are described. A contribution of this paper is to study the technical requirements of suitable data for decision-making. The outcomes of presented study are identifying the characteristics of input data to predictive maintenance in the era of big data and the discussion of these characteristics in the condition monitoring for off-shore wind energy.


Predictive maintenance Big data Off-shore wind energy Data analytics 


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Elaheh Gholamzadeh Nabati
    • 1
  • Klaus Dieter Thoben
    • 2
  1. 1.International Graduate School of Dynamics in LogisticsUniversity of BremenBremenGermany
  2. 2.BIBA- Bremer Institute für Produktion und Logistik GmbHBremenGermany

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