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Method for estimating risk indicators for failure modes in a wind turbine and their use for rescheduling the planned maintenance

M.A. Sanz-Bobi, R.J. Andrade Vieira

1st International Conference on Through-life Engineering Services, Cranfield (Reino Unido). 05 noviembre 2012


Resumen:
A wind turbine is affected during its life by several external and internal conditions that can induce failure modes, or at least, contribute to the presence of one or more symptoms that can cause a certain amount of stress in its components and therefore facilitate the development of such failure modes. This paper presents a new method able to estimate the health condition of components in a wind turbine based on the on-line information collected about their observable lives. The proposed method uses the information coming in real-time in order to characterize risk indicators for failure modes of the main components of a wind turbine operating under different normal previously fitted with real data about the typical life of a component carrying out its functions within dynamically rescheduled according to the observed values of the risk indicators in a component using the resources that are really needed. These are the main foundations for a new maintenance model able to integrate in a natural way, information coming from the operation and maintenance of a component, trying to keep the availability of the asset, and hence its value in use, as high as possible. Some real examples of application ot these new concepts in components of a wind turbine will be described.


Palabras clave: Wind turbine diagnosis, maintenance, normal behaviour models, anomaly detection, failure mode risk indicator


Fecha de publicación: noviembre 2012.



Cita:
M.A. Sanz-Bobi, R.J. Andrade Vieira, Method for estimating risk indicators for failure modes in a wind turbine and their use for rescheduling the planned maintenance, 1st International Conference on Through-life Engineering Services, Cranfield (Reino Unido). 05-06 Noviembre 2012.


    Líneas de investigación:
  • *Inteligencia artificial aplicada al mantenimiento, diagnóstico y fiabilidad

IIT-12-132A

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