Damage detection in operational wind turbine blades using a new approach based on machine learning

Chandrasekhar, K., Stevanovic, N., Cross, E. et al. (2 more authors) (2021) Damage detection in operational wind turbine blades using a new approach based on machine learning. Renewable Energy, 168. pp. 1249-1264. ISSN 0960-1481

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2020 Elsevier Ltd. This is an author produced version of a paper subsequently published in Renewable Energy. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: structural health monitoring; wind turbine blades; machine learning; Gaussian processes; SCADA
Dates:
  • Accepted: 27 December 2020
  • Published (online): 31 December 2020
  • Published: May 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/R003645/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/S001565/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/R004900/1
Depositing User: Symplectic Sheffield
Date Deposited: 05 Jan 2021 12:52
Last Modified: 28 Jan 2022 13:44
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.renene.2020.12.119

Download

Export

Statistics