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Current and vibration analysis for a preventive and predictive condition based maintenance in offshore wind farms – KAStrion





Submission summary

KAStrion project aims at maximizing the production time of offshore wind turbine farms by delivering a complete solution build upon:
• A stand-alone analysis system which delivers an innovative on-site pre-diagnostic of the machine based on a multi-modal spectral monitoring technology,
• Connected to a tailored diagnostic center which delivers a reliability prediction of the farm.

The production energy of wind farm is becoming more and more important in several countries. Its impact on the electrical grid grows every day, at the country and continental grid scale. The economic value of the wind farm does not only reside in the amount of energy produced but also in the capacity to forecast this energy. The forecast is built on production capacity available and wind prediction. The availability of the equipment and therefore the capacity to plan the maintenance based on a reliable condition monitoring of the equipment are crucial challenges.

In that context, KAStrion aims at improving the production capacity forecast with a set of innovative technical solutions that would improve existing ones or eventually replace them.
The challenge is to consider deterioration modeling (prognosis and residual life prediction. KAStrion will aim to investigate the feasibility to link condition monitoring approaches to deterioration-based reliability models for online reliability estimation and residual life prediction.

Project coordinator

Madame Nadine MARTIN (Grenoble Images Signal Parole Automatique) –

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


GIPSA-Lab Grenoble Images Signal Parole Automatique
INPT Institut de Recherche en Informatique de Toulouse
MECAL MECAL Wind Farm Services BV - Pays Bas

Help of the ANR 150,001 euros
Beginning and duration of the scientific project: July 2012 - 42 Months

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