Skip to Main content Skip to Navigation
Conference papers

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

Koceila Abid 1, 2 Moamar Sayed Mouchaweh 1 Laurence Cornez 2
2 LS2D - Laboratoire Sciences des Données et de la Décision
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
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.
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download

https://hal-cea.archives-ouvertes.fr/cea-02174945
Contributor : Marie-France Robbe <>
Submitted on : Friday, July 5, 2019 - 1:43:15 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:53 PM

File

article_KoceilaAbid_2018_IOSTR...
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

155

Files downloads

572