Incorporating Machine Learning into Vibration Detection for Wind Turbines. (16th February 2022)

• Record Type: Journal Article
• Authors: Vives, J.
• Other Names: Vaiana Nicolò Academic Editor.
• Is Part Of: Modelling and simulation in engineering. Volume 2022(2022)
• Publication Date: 2022-02-16
• Subjects: Engineering -- Simulation methods -- Periodicals; Engineering -- Mathematical models -- Periodicals; 620.004
• DOI: 10.1155/2022/6572298
• Languages: English
• ISSNs: 1687-5591

Abstract

Abstract : 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.