Application of anomaly technique in wind turbine bearing fault detection

Download
Full text for this resource is not available from the Research Repository.
Export

Purarjomandlangrudi, Afrooz ORCID: 0000-0003-3028-1408, Nourbakhsh, Ghavameddin, Ghaemmaghami, Houman and Tan, Andy (2015) Application of anomaly technique in wind turbine bearing fault detection. In: IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, 29 Oct - 1 Nov 2014, Texas, USA.

Dimensions Badge

Altmetric Badge

Item type Conference or Workshop Item (Paper)
URI https://vuir.vu.edu.au/id/eprint/44212
DOI 10.1109/iecon.2014.7048774
Official URL https://ieeexplore.ieee.org/document/7048774
ISBN 9781479940325
Subjects Current > FOR (2020) Classification > 4011 Environmental engineering
Current > FOR (2020) Classification > 4611 Machine learning
Current > Division/Research > VU School of Business
Keywords wind turbines, renewable energy, energy efficiency, machine learning
Citations in Scopus 5 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login

CORE (COnnecting REpositories)