Vibration Analysis for Fault Detection of Wind Turbines by Combining Machine-Learning Techniques and 3D Scanning Laser.

Autor: Vives J; Department of Systems Engineering and Automation, University Polytechnic of Valencia, Camino de Vera S/N, Valencia 46022, Spain., Roses Albert E; Engineering Research Team, Florida Universitària, Catarroja 46470, Spain., Quiles E; Red Engineering Technology Limited, Wolverton, Milton Keynes MK12 5DJ, UK., Palací J; Red Engineering Technology Limited, Wolverton, Milton Keynes MK12 5DJ, UK., Fuster T; Red Engineering Technology Limited, Wolverton, Milton Keynes MK12 5DJ, UK.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Dec 26; Vol. 2022, pp. 2093086. Date of Electronic Publication: 2022 Dec 26 (Print Publication: 2022).
DOI: 10.1155/2022/2093086
Abstrakt: With this research, we apply range-resolved interferometry (RRI) to the maintenance of wind turbines using some of the most relevant machine-learning (ML) techniques. The degeneration of electrical and mechanical components of wind turbines can be predicted, detected, and anticipated using this method of automatic and autonomous learning. The vibrations in two different failure states are detected with the help of a scanner laser. In-process measurements taken by RRI agree with manual measurements, laser scanning measurements, and in-process hand measurements made following each working cycle. Consequently, the proposed method will be very useful for monitoring and diagnosing faults in wind turbines. The system will also be able to perform low-cost in-process measurements.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Javier Vives et al.)
Databáze: MEDLINE
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