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Conference Papers Year : 2019

Fault Prognostics for the Predictive Maintenance of Wind Turbines: State of the Art

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Abstract

Reliability and availability of wind turbines are crucial due to several reasons. On the one hand, the number and size of wind turbines are growing exponentially. On the other hand, installation of these farms at remote locations, such as offshore sites where the environment conditions are favorable, makes maintenance a more tedious task. For this purpose, predictive maintenance is a very attractive strategy in order to reduce unscheduled downtime and maintenance cost. Prognostic is an online technique that can provide valuable information for proactive actions such as the current health state and the Remaining Useful Life (RUL). Several fault prognostic works have been published in the literature. This paper provides an overview of the different prognostic phases, including: health indicator construction, degradation detection, and RUL estimation. Different prognostic approaches are presented and compared according to their requirements and performance. Finally, this paper discusses the suitable prognostic approaches for the proactive maintenance of wind turbines, allowing to address the latter challenges.
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cea-02174945 , version 1 (05-07-2019)

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Koceila Abid, Moamar Sayed Mouchaweh, Laurence Cornez. Fault Prognostics for the Predictive Maintenance of Wind Turbines: State of the Art. Joint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2018, Sep 2018, Dublin, Ireland. pp.113-125, ⟨10.1007/978-3-030-14880-5_10⟩. ⟨cea-02174945⟩
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