Incorporating Machine Learning into Vibration Detection for Wind Turbines

With machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine...

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Bibliographic Details
Published inModelling and Simulation in Engineering Vol. 2022; pp. 1 - 8
Main Author Vives, J.
Format Journal Article
LanguageEnglish
Published New York Hindawi 16.02.2022
John Wiley & Sons, Inc
Wiley
Subjects
Air-turbines
Algorithms
Analysis
Artificial intelligence
Buildings and facilities
Classification
Data mining
Defective products
Degeneration
Failure
Fault diagnosis
Global temperature changes
Machine learning
Methods
Support vector machines
Turbines
Vibration
Wind power
Wind turbines
Online AccessGet full text
ISSN1687-5591
1687-5605
1687-5605
DOI10.1155/2022/6572298

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Summary:With machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine learning algorithms adapted to the different components and faults of wind turbines, this study evaluates different methodologies for monitoring, supervision, and fault diagnosis.
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ISSN:1687-5591
1687-5605
1687-5605
DOI:10.1155/2022/6572298