print

Multi-sensor fault diagnosis of induction motors using random forests and support vector machine

author
Saberi, Alireza Nemat
Sandirasegaram, Sarvavignoban
Belahcen, Anouar
Vaimann, Toomas
Sobra, Jan
statement of authorship
Alireza Nemat Saberi, Sarvavignoban Sandirasegaram, Anouar Belahcen, Toomas Vaimann, Jan Sobra
source
2020 International Conference on Electrical Machines (ICEM), 23-26 august 2020, Gothenburg, Sweden : online : proceedings
location of publication
Danvers
publisher
IEEE
year of publication
2020
pages
p. 1404–1410
conference name, date
International Conference on Electrical Machines (ICEM), 23-26 august 2020
conference location
Gothenburg, Sweden
url
https://doi.org/10.1109/ICEM49940.2020.9270689
subject term
vead
diagnostika (tehnika)
vibratsioon
rootorid
RFID-tehnoloogia
keyword
support vector machines
fault diagnosis
vibrations
feature extraction
radio frequency identification
rotors
loading
ISSN
2381-4802
ISBN
978-1-7281-9945-0
notes
Bibliogr.: 23 ref
TTÜ department
materjali- ja keskkonnatehnoloogia instituut
language
inglise
Uurimisrühm
Electrical machines research group
Saberi, A.N., Sandirasegaram, S., Belahcen, A., Vaimann, T., Sobra, J. Multi-sensor fault diagnosis of induction motors using random forests and support vector machine // 2020 International Conference on Electrical Machines (ICEM), 23-26 august 2020, Gothenburg, Sweden : online : proceedings. Danvers : IEEE, 2020. p. 1404–1410. https://doi.org/10.1109/ICEM49940.2020.9270689