Posters - WindEurope Annual Event 2024

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Come meet the poster presenters to ask them questions and discuss their work

We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in the academic community. We look forward to seeing you there!

PO009: Challenges on prognostics and health management for wind turbine components

Urko Leturiondo, Monitoring Team Leader, Ikerlan


Maintenance tasks for wind turbines (WTs) should be optimized, moving from corrective and preventive maintenance to a predictive maintenance approach. In this context, the engineering field of prognostics and health management (PHM), which covers the group of techniques for monitoring component wear evolution, has a crucial role. Thanks to PHM, it is possible to predict the remaining useful life (RUL) of components and assets from historical and actual operating data. Different methods are used for PHM, mainly categorized in physics-based, knowledge-based, data-driven, and hybrid methods. It should be highlighted that, among them, data-driven methods are increasingly being used thanks to the exponential growth in the usage of artificial intelligence (AI). However, there is no consensus on the classification of data-driven methods for RUL calculation. Regarding the data to be used by the data-driven techniques to monitor and predict faults in WT components, supervisory control and data acquisition (SCADA) data and CM data are the main ones. Despite the fact that any of all these sources have been already analyzed, the fusion of SCADA and CM data is conceived, but the innovative aspects of fusing both types of data are clearly shown due to the tiny amount of references in the literature. The application of PHM strategies in wind turbines is still limited. It is worth mentioning the great importance of fatigue-based failure modes analyses in different components according to the literature. Although PHM strategies have been around for years, their development continues to be a current challenge, especially in industrial applications. The promising capabilities of digitalization and AI generate a wide range of possibilities from a methodological and algorithmic point of view. The results that will be generated through this research line focused on the usage of AI technologies for RUL estimation of WT components.

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