Y. Vidal, M. Rodgers; Editorial: Wind turbine fault and damage diagnosis and prognosis, Frontiers in Energy Research, Frontiers in Energy Research Section: Wind Energy 10:1038271. doi: 10.3389/fenrg.2022.1038271, 2022.

Source: Frontiers

Editorial on the Research Topic

Wind turbine fault and damage diagnosis and prognosis

1Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besós (CDB), Barcelona, Spain
2Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
3Wind Energy Institute of Canada, Summerside, PE, Canada

Front. Energy Res., 28 September 2022
Sec. Wind Energy
Volume 10 – 2022 | https://doi.org/10.3389/fenrg.2022.1038271

To remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. Thus, it is essential to develop robust and cost-effective prognostic and health management strategies, both in terms of their structure and their components.

On the one hand, the purpose of wind turbine structural health monitoring (SHM) is to detect, locate, and characterize structural damage, so that maintenance operations can be performed in due time. SHM has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes, improving structural reliability and life cycle management. In the near future, SHM has the potential to be a wind energy harvester, in particular for offshore wind turbines. On the other hand, fault diagnosis and prognosis strategies based on condition based maintenance for the different systems (gearbox, main bearing, generator, etc.) of the wind turbine are crucial. Vibration-based condition monitoring is a well-established strategy, but it usually relies on high-sampled data (>10
kHz) leading to a large amount of data from a large number of sensors. Finding patterns in such multivariable datasets is a challenge under the variety of operational modes and environmental conditions that wind turbines are subject to.

This Research Topic invited contributions that addressed wind turbine fault and damage prognosis and diagnosis through novel contributions and innovative applications covering, but not limited to, any of the following topics around wind turbines: condition monitoring, structural health monitoring, fault diagnosis, damage diagnosis, machine learning, deep learning, model-based.

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