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LightGBM-based fault diagnosis of rotating machinery under changing working conditions using modified recursive feature elimination

author
Saberi, Alireza Nemat
Belahcen, Anouar
Sobra, Jan
Vaimann, Toomas
statement of authorship
Alireza Nemat Saberi, Anouar Belahcen, Jan Sobra, Toomas Vaimann
source
IEEE Access
publisher
IEEE
journal volume number month
vol. 10
year of publication
2022
pages
p. 81910-81925
url
https://doi.org/10.1109/ACCESS.2022.3195939
subject term
elektriajamid
masinad
töökindlus
tehisõpe
keyword
electrical machines
bearings
fault diagnosis
feature importance
gradient boosting
hyperparameter optimization
LightGBM
machine learning
ISSN
2169-3536
notes
Bibliogr.: 54 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
Journal metrics at Scopus
Article at Scopus
WOS
Journal metrics at WOS
Article at WOS
category (general)
Engineering
Computer science
Materials science
category (sub)
Engineering. General engineering
Computer science. General computer science
Materials science. General materials science
kvartiil
Q1
TTÜ department
elektroenergeetika ja mehhatroonika instituut
language
inglise
Uurimisrühm
Electrical machines research group
Saberi, A. N., Belahcen, A., Sobra, J., Vaimann, T. LightGBM-based fault diagnosis of rotating machinery under changing working conditions using modified recursive feature elimination // IEEE Access (2022) vol. 10, p. 81910-81925. https://doi.org/10.1109/ACCESS.2022.3195939