Wind Turbine Generator Bearing Anomaly Detection and Explanation Using RRCF Approach - Archive ouverte HAL
Proceedings/Recueil Des Communications Année : 2024

Wind Turbine Generator Bearing Anomaly Detection and Explanation Using RRCF Approach

Résumé

Nowadays, machines are everywhere, and while their design and construction are a major challenge, maintenance has to be one of the main problems to solve during their life cycle. Reactive maintenance is simply waiting for a component to fail before taking action, which is not only costly but also leads to downtime and can result in lost revenue. Predictive maintenance, on the other hand, is proactive and can minimise downtime and extend the life of the equipment. However, predictive maintenance relies on reliable forecasts to avoid over-scheduling. This problem is particularly interesting for wind farms, as they are usually located in remote areas, making access to repairs difficult and time-consuming. In this paper, we discuss predictive maintenance in the context of a wind farm and propose and develop a diagnostic and recommendation model based on the random-cut forest approach that can assist an operator in his decision-making process and provide him with information to understand why he should make a particular decision. The methodology's effectiveness is verified and applied to a realm dataset of wind farms.

Domaines

Automatique
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Dates et versions

hal-04707121 , version 1 (24-09-2024)

Identifiants

Citer

Raubertin Randrianandraina, Lala Rajaoarisoa, Lala Rajaoarisoa, Moamar Sayed-Mouchaweh. Wind Turbine Generator Bearing Anomaly Detection and Explanation Using RRCF Approach. 2023 International Conference on Machine Learning and Applications (ICMLA), IEEE, pp.1574-1581, 2024, ⟨10.1109/ICMLA58977.2023.00238⟩. ⟨hal-04707121⟩
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