STARDUST II - Spatial and Temporal Assessment
of high Resolution Depth profiles Using novel Sampling Technologies The fate of pollutants in fluvial and marine sediments in cross-border zones
Integrated condition monitoring of a fleet of offshore wind turbines with focus on acceleration streaming processing
Helsen, J.; Gioia, N.; Peeters, C.; Jordaens, P.J. (2017). Integrated condition monitoring of a fleet of offshore wind turbines with focus on acceleration streaming processing. Journal of Physics: Conference Series 842: 012052. https://dx.doi.org/10.1088/1742-6596/842/1/012052
Particularly offshore there is a trend to cluster wind turbines in large wind farms, and in the near future to operate such a farm as an integrated power production plant. Predictability of individual turbine behavior across the entire fleet is key in such a strategy. Failure of turbine subcomponents should be detected well in advance to allow early planning of all necessary maintenance actions; Such that they can be performed during low wind and low electricity demand periods. In order to obtain the insights to predict component failure, it is necessary to have an integrated clean dataset spanning all turbines of the fleet for a sufficiently long period of time. This paper illustrates our big-data approach to do this. In addition, advanced failure detection algorithms are necessary to detect failures in this dataset. This paper discusses a multi-level monitoring approach that consists of a combination of machine learning and advanced physics based signal-processing techniques. The advantage of combining different data sources to detect system degradation is in the higher certainty due to multivariable criteria. In order to able to perform long-term acceleration data signal processing at high frequency a streaming processing approach is necessary. This allows the data to be analysed as the sensors generate it. This paper illustrates this streaming concept on 5kHz acceleration data. A continuous spectrogram is generated from the data-stream. Real-life offshore wind turbine data is used. Using this streaming approach for calculating bearing failure features on continuous acceleration data will support failure propagation detection.
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STARDUST II is a project funded by the INTERREG III A programme (France/Walloon Region/Flanders
French-Flemish subprogramme) of the European Community's Regional Development Fund.
Hosted by the Flanders Marine Institute (VLIZ)
